Pith. sign in

REVIEW 5 major objections 6 minor 202 references

Higher-Order Graph Databases

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that any higher-order graph—hypergraph, node-tuple collection, or subgraph collection—can be lowered losslessly into a heterogeneous graph, and therefore into a labeled property graph, making higher-order data natively…

desk verdict A plausible and useful systems idea—storing higher-order structures as typed nodes in an LPG—but the formal losslessness claims only hold up for hypergraphs; the node-tuple and subgraph encodings have real definitional bugs and the evaluation lacks baselines. read the letter →

arxiv 2506.19661 v1 pith:C2WSY4KY submitted 2025-06-24 cs.DB cs.IRcs.LGcs.SI

Maciej Besta , Shriram Chandran , Jakub Cudak , Patrick Iff , Marcin Copik , Robert Gerstenberger , Tomasz Szydlo , Jürgen Müller
show 1 more author
Torsten Hoefler
This is my paper · ORCID
classification cs.DBcs.IRcs.LGcs.SI
keywords higher-ordergraphdatabaseshypergraphsnode-tuplecollectionssubgraphlabeledpropertyheterogeneousneuralnetworksACIDtransactions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a new class of database systems, higher-order graph databases (HO-GDBs), that let ordinary graph databases store and query higher-order structures—hyperedges, node tuples, subgraph collections, and simplicial complexes—as first-class citizens. Its central claim is that every such structure can be lowered, without information loss, into a heterogeneous graph, which is representable in the labeled property graph model that existing engines already support. The paper proves that any pair of mutual-inverse, isomorphism-preserving lowering and lifting maps is lossless, and gives explicit constructions for each higher-order model. It then builds a prototype on a standard property-graph engine and reports that higher-order query workloads scale with process count, and that a higher-order graph neural network trained through the database cuts test loss by about 44% compared with a first-order baseline. The reason to care is that the approach turns higher-order analytics into a data-management feature without requiring a new database engine.

What carries the argument

The carrying mechanism is the lowering/lifting pair between higher-order graphs and heterogeneous graphs. Lowering renames every higher-order object as a typed node and every membership or containment relation as a typed edge: incidence edges for hyperedges, ordered node-membership edges (with an index $i$) for tuples, and node-membership, edge-membership, and subgraph-adjacency edges for subgraph collections. Since heterogeneous graphs are labeled property graphs with labels acting as types, standard storage, indexing, and query engines apply unchanged. The logical load-bearing identity is Theorem 4.1: if lowering and lifting are mutual inverses and isomorphism-preserving, the transformation is lossless.

What would settle it

Take two node-tuple collections that differ only in the order of elements inside one tuple, run the paper's encoding and decoding on both, and check whether the decoded collections come back in the original order and whether the encoded versions are distinguishable. If the order is lost, or the two encoded graphs are indistinguishable, the transformation is not lossless.

Watch

Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that any higher-order graph can be encoded as a multi-partite heterogeneous graph, hence as a labeled property graph, in a way that is lossless. For hypergraphs, simplicial complexes, node-tuple collections, and subgraph collections, the paper defines a lowering map $L^{\top}$ and a lifting map $L$, and proves in Theorem 4.1 that if the two maps are mutual inverses and isomorphism-preserving, the round trip preserves the semantics of the original structure. In the lowered representation a hyperedge becomes a node typed “hyperedge” connected to its members by incidence edges; a node tuple becomes a node whose membership edges carry the tuple order; a subgraph becomes a node connected to the vertices and edges it contains. Because heterogeneous graphs are trivially representable in the labeled property graph model, higher-order objects can carry properties, be indexed, be linked to other entities, and be updated through the same storage and query machinery as ordinary graph data.

Load-bearing premise

Everything rests on the assertion that the round-trip encoding for node-tuples and subgraph collections is exact and structure-preserving; the paper states that the proof is similar to the hypergraph case without giving it in detail.

Editorial extensions

If this is right

  • Existing labeled-property-graph databases can offer native higher-order support without flattening or external preprocessing.
  • Higher-order structures become first-class citizens: they can be labeled, attributed, indexed, linked to ontologies or similarity graphs, and queried through standard interfaces.
  • Online transactional operations on higher-order entities can inherit ACID semantics by bundling all low-level changes to the lowered representation into a single transaction.
  • Analytical workloads such as hypergraph path traversal and higher-order graph neural network training can run directly against the database, with one-time lowering costs reported at under 5% of OLTP runtime and under 1% of OLAP runtime.
  • On a 400-molecule benchmark, the higher-order GNN trained through the system reduces test loss by about 44% relative to a first-order GNN, indicating faster convergence and better accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the losslessness claim holds for all four models, the same lowering construction is portable to any engine that stores heterogeneous or labeled-property graphs, including RDF stores and eventually consistent graph systems; the paper gestures at this generality but does not implement it.
  • The expansion in vertex and edge counts under lowering suggests that query cost will concentrate on membership joins, so a dedicated index over higher-order type/property pairs would be a natural next engineering step beyond the paper's label-property indexing.
  • The ACID argument assumes that wrapping low-level operations in one transaction transfers the backend's isolation guarantees to the higher-order level; a stress test with concurrent transactions over overlapping tuples or subgraphs would settle whether that inheritance is complete.
  • The reported 44% test-loss improvement comes from one architecture and one 400-molecule dataset; repeating the comparison across more higher-order models and datasets would show how general the accuracy gain is.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes a new class of graph database systems, HO-GDBs, that support higher-order structures (hypergraphs, simplicial complexes, node-tuple collections, subgraph collections) by "lowering" them into heterogeneous graphs over the LPG model and "lifting" them back. The central formal claim is that these transformations are lossless and isomorphism-preserving (Theorem 4.1), with constructions in Appendix A. The paper also describes a Neo4j-based prototype, discusses ACID guarantees, gives complexity bounds in Table 2, and evaluates OLTP scaling and HO-GNN accuracy on real datasets, reporting a 44% test-loss reduction.

Significance. If fully established, the paper would provide a broadly applicable design pattern: higher-order structures become first-class citizens in existing LPG-based graph databases without flattening, with only a thin API layer on top of a standard backend. The hypergraph construction in Appendix A.2 is explicit and correctly proved, the implementation is released, and the evaluation is substantial and uses realistic datasets (ZINC, MAG-10). However, the formal support for node-tuples and subgraph collections is incomplete, and the complexity and ACID claims are asserted rather than derived. The general claim that 'any HO graph can be transformed into a multi-partite heterogeneous graph' without information loss currently goes beyond what is proven.

major comments (5)
  1. [Appendix A.4 / Section 4.2] The node-tuple lowering is defined on a 'tripartite heterogeneous multigraph', but the target model in Section 4.2 is G=(V,E,τ,κ) with E⊂V×V, which does not admit parallel edges. For a tuple t=(v3,v3) (as shown in Figure 2), EG contains only the single membership edge (v3,t), and the lifting rule then reconstructs t as a tuple of arity 1, so the roundtrip is not lossless. If EG is instead intended as a multiset, the definitions of τ, κ, xG, graph isomorphism, and the 'similar' proof of Theorem A.2 are not given for multigraphs. The losslessness of node-tuple lowering/lifting is therefore not established.
  2. [Appendix A.5 / Theorem A.3] The lifting L_S is not well-typed. E_H is recovered as a set of endpoint pairs {(u,v) | ...}, yet the recovery formula for S uses e∈E_H in the membership test (e,s)∈E_G, while E_G contains edge-vertices rather than endpoint pairs. Similarly, F={(u,v) | u,v∈S, ...} treats elements of S, which are pairs of vertex/edge sets, as vertices of E_G. Because lowering maps original edges to edge-vertices, a lossless lifting must restore edge identities and edge features, which the pair-based recovery of E_H cannot do. Theorem A.3 is stated without a proof ('The proof is similar to theorem A.1'), and the differences from the hypergraph case are exactly where the missing argument is needed. Hence Theorem 4.1 cannot transfer losslessness to subgraph collections in the current form.
  3. [Section 4.2 / Theorem 4.1] The theorem is essentially a restatement of the assumptions: once L and L⊤ are assumed to be mutual inverses and isomorphism-preserving, losslessness follows immediately, and the proof's bidirectional chain reduces to H1≅H2 ⇔ H1≅H2. All substantive content is delegated to the appendix constructions, which are incomplete for two of the four supported structures (see the two comments above). The central claim 'any HO graph can be transformed into a multi-partite heterogeneous graph' should be narrowed to hypergraphs, or the gaps in Appendices A.4 and A.5 must be closed.
  4. [Section 7 / Table 2] The complexity bounds in Table 2 are asserted without derivation, despite the text promising 'Full derivations are in Appendix A'. The appendix gives operation listings but no complexity analysis. The caption's blanket assumption that hyperedges, node-tuples, and subgraphs contain O(n) nodes is not derived or justified, and some cells (e.g., Edge Insert/Delete for Hypergraph and Simplicial Complexes) are left blank without explanation. If 'rigorous theoretical analysis' is claimed, each O(·) entry in Table 2 should be derived, or the claims should be softened.
  5. [Section 5.2] The ACID compliance claim is not established. Section 5.2 argues that bundling low-level Neo4j operations into a single transaction transfers Neo4j's ACID guarantees to HO-level operations, but no argument is given for HO-level isolation when concurrent transactions manipulate overlapping HO constructs (e.g., two transactions modifying subgraphs that share vertices), nor for atomicity and consistency of the delete policy when HO entities contain repeated elements. Since the abstract promises ACID compliance, this transfer needs to be made precise, or the claim should be explicitly scoped to the guarantees that can actually be derived from the backend.
minor comments (6)
  1. [Appendix A.2] The notation is inconsistent: the appendix uses L⊤_H for lowering and L_H for lifting, but the proof of Theorem A.1 contains 'G1 = LT_H(H1)' and an implication arrow in the wrong direction; please unify notation and clean up the proof.
  2. [Section 4.2] The informal definition of higher-order graph isomorphism ('one-to-one mapping of entities') is incomplete because it does not explicitly require preservation of incidence, labels, or properties; the appendix proofs correctly use incidence and feature preservation, so the informal definition should be corrected.
  3. [Section 8.3] The text states that 'total read time' decreases linearly in Figure 4a, but the figure's y-axis is labeled 'Throughput (queries/s)'; please clarify which quantity is plotted.
  4. [Section 10] In the conclusion, 'HO-GBD could be further extended' should read 'HO-GDB could be further extended'.
  5. [Section 2.2] Node-tuples are defined with T⊆⋃_{k≥2} V^k, but the discussion in Section 5.2.2 and the lifting rule in Appendix A.4 contemplate arity-1 tuples; please specify whether arity-1 tuples are part of the model and how they are lowered and lifted.
  6. [Table 2] The column labeled 'Lowering & Lifting Time & Storage' mixes time and storage complexity in a single column; separating the two would make the table significantly clearer.

Circularity Check

1 steps flagged · score 4.0 of 10

Theorem 4.1 is self-definitional; losslessness for node-tuples and subgraphs is asserted via 'similar' proofs rather than derived.

  1. self definitional [Section 4.2, Theorem 4.1 and its proof]
    "This losslessness is achieved by ensuring that the transformation is isomorphism-preserving... Theorem 4.1. Let L : G → H and L⊤ : H → G be isomorphism preserving lifting and lowering functions such that L◦L⊤ = idH and L⊤◦L = idG... Then L and L⊤ provide lossless transformations between higher-order and heterogeneous graphs."

    The paper defines losslessness as isomorphism preservation, so the theorem's conclusion is exactly its hypothesis: it assumes L and L⊤ are already isomorphism-preserving mutual inverses and then 'proves' losslessness. The proof only applies the assumed isomorphism-preservation of L and L⊤ to both sides and recovers H1 ≅ H2; no independent fact is established, and the theorem does not show that any such maps exist. Since the existence of lossless encodings is the paper's central claim, this theorem makes that claim definitional rather than derived.

full rationale

The central claim depends on two layers. The first layer, Theorem 4.1, is self-definitional: losslessness is defined as isomorphism preservation, and the theorem assumes isomorphism-preserving mutual inverses and concludes losslessness, so its proof is a restatement of its hypothesis. The second layer, the appendix constructions, contains the real content. For hypergraphs (Appendix A.2), the bipartite incidence-graph lowering and lifting is explicit and Theorem A.1 is proved in detail; simplicial complexes (A.3) inherit that argument. For node-tuples (A.4) and subgraph collections (A.5), however, the paper only states 'The proof is similar to theorem A.1' (A.4) and 'The proof is similar to theorem A.1' (A.5), so losslessness for two of the four advertised structures is asserted rather than derived. In addition, the A.4 construction calls the target a 'tripartite heterogeneous multigraph' while Section 4.2 defines heterogeneous graphs with E⊂V×V and no parallel edges, and repeated tuple elements (e.g., (v3,v3) in Figure 2) would require parallel membership edges; A.5's lifting uses e both as an edge-pair in EH and as a vertex in EG. These are correctness gaps rather than circular reductions. The experimental evaluation is self-contained and benchmarked against external datasets (MAG-10, ZINC) and an external GNN architecture [80]; self-citations such as [39] are used only for standard definitions or context and are not load-bearing. I therefore rate 4: one definitional circular theorem plus unproven 'similar' lemmas, while substantial independent encoding and system content remains.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities; the HO constructs are existing models (hypergraphs, subgraphs, tuples) and the 'lifting' and 'lowering' are formalized transformations, not invented entities. The main assumptions are the standard definitions of the data models and the delegation of ACID properties to the underlying Neo4j engine.

assumptions (5)
  • standard math The Labeled Property Graph (LPG) formal model as defined in Section 2.1 is taken as the underlying data model.
    The paper builds on the standard LPG definition from the cited literature and uses it to define heterogeneous graphs.
  • domain assumption Higher-order structures (hypergraphs, simplicial complexes, node-tuples, subgraph collections) are defined as in Section 2.2.
    These definitions are the starting point for the lowering/lifting constructions; the paper does not justify them beyond citing prior work.
  • ad hoc to paper For the complexity analysis, every entity and relationship has O(d) properties and hyperedges, node-tuples, and subgraphs contain O(n) nodes.
    This assumption is stated in the caption of Table 2 and is not derived from any source; it is a modeling choice that simplifies the stated bounds.
  • domain assumption Neo4j's transactional semantics and MVCC provide the necessary ACID guarantees when low-level operations are bundled into a single transaction.
    Section 5.2 relies on the backend's ACID behavior without proving that the wrapping preserves isolation and consistency at the HO level.
  • standard math Isomorphism preservation of L and L^T implies losslessness of the transformation.
    This logical implication is correct, but it is essentially the definition of isomorphism-preserving, making Theorem 4.1 close to a tautology.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Higher-Order Graph Databases." pith.science (2026). https://pith.science/paper/C2WSY4KY

@misc{pith2026250619661,
  author       = {Pith},
  title        = {Pith review of: Higher-Order Graph Databases},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C2WSY4KY}},
  note         = {Machine review of arXiv:2506.19661}
}
read the original abstract

Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order (one-hop) relations, which are crucial for tasks such as subgraph counting, polyadic modeling, and HO graph learning. We address this by introducing a new class of systems, higher-order graph databases (HO-GDBs) that use lifting and lowering paradigms to seamlessly extend traditional GDBs with HO. We provide a theoretical analysis of OLTP and OLAP queries, ensuring correctness, scalability, and ACID compliance. We implement a lightweight, modular, and parallelizable HO-GDB prototype that offers native support for hypergraphs, node-tuples, subgraphs, and other HO structures under a unified API. The prototype scales to large HO OLTP & OLAP workloads and shows how HO improves analytical tasks, for example enhancing accuracy of graph neural networks within a GDB by 44%. Our work ensures low latency and high query throughput, and generalizes both ACID-compliant and eventually consistent systems.

Figures

Figures reproduced from arXiv: 2506.19661 by the authors.

Figure 1
Figure 1. Left side: An example Labeled Property Graph (LPG) with added higher-order (HO) structures. Right side: the same LPG with its HO structures modeled as LPG vertices, edges, labels, and properties. This enables these HO structures to be stored and used seamlessly in most graph databases. that allow for multiple node and edge types. Heterogeneous graphs are widely supported as first-class citizens in graph database sys… view at source ↗
Figure 2
Figure 2. Overview of the introduced HO-GDB paradigm and new class of systems. Top row: An example plain (i.e. non-HO) graph and the same graph with example HO structures. Top-mid row (“Transitional N-partite Graphs”): an intermediate stage of translating the HO structures into a format digestible by any GDB. Bottom-mid row (“Heterogeneous Graphs”): the result of the “lowering” transformation that encodes HO structures as het… view at source ↗
Figure 3
Figure 3. An illustration of two higher-order (HO) practical use cases: (left) quality incidents as subgraph collections (details in Section 3.1), [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 3
Figure 3. Figure 3: The graph encodes interrelated entities such as pro [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 4
Figure 4. Figure 4: Higher-Order OLTP results. 0 100 200 300 Epochs 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 Learning Rate Learning Rate GNN w/ HO GNN w/o HO 0 100 200 300 Epochs 0.1 0.2 0.3 0.4 0.5 0.6 Loss Loss GNN w/ HO GNN w/o HO 0 100 200 300 Epochs 0.2 0.4 0.6 0.8 1.0 1.2 Validatio…
Figure 5
Figure 5. Figure 5: Higher-Order OLAP results (HO GNNs). Query Type mostly-reads mixed write-heavy Reads 93.75% 50% 25% Retrieve node 46.87% 25% 12.5% Retrieve hyperedge 46.88% 25% 12.5% Writes 6.25% 50% 75% Update node 3.13% 42.19% 57.81% Add hyperedge 3.12% 7.81% 17.19% [PITH_FULL_IMAG…
Figure 2
Figure 2. Figure 2: If each hyperedge has size O(n), the lowered het￾erogeneous graph G has n + nH vertices and Σhnh ∈ O(nnH) edges. the memory complexities of the heterogeneous graph is O(d(nnH)), while the lowering and lifting run in O(d(nnH)) time. Graph updates can be performed by the…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

202 extracted references · 33 canonical work pages

  1. [1]

    Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang, Wei Hu, and Chengkai Li. 2020. Realistic Re-Evaluation of Knowledge Graph Completion Methods: An Experimental Study. InProceedings of the International Conference on Management of Data(Portland, OR, USA) (SIGMOD ’20). Association for Computing Machinery, New York, NY, USA, 1995–2010. https://doi.org/...

    arXiv 2020

  2. [2]

    AliPay. 2025. TuGraph. https://github.com/TuGraph-family/tugraph- db. accessed: 2025-04-15

    2025

  3. [3]

    Altair Engineering Inc. 2025. Altair Graph Studio. https://altair.com/ altair-graph-studio. accessed 2025-04-14

    2025

  4. [4]

    Unai Alvarez-Rodriguez, Federico Battiston, Guilherme Ferraz de Ar- ruda, Yamir Moreno, Matjaž Perc, and Vito Latora. 2021. Evolutionary Dynamics of Higher-Order Interactions in Social Networks.Nature Hu- man Behaviour5, 5 (Jan. 2021), 586–595. https://doi.org/10.1038/s41562- 020-01024-1

    doi:10.1038/s41562- 2021

  5. [5]

    Amazon. 2025. Amazon Neptune. https://aws.amazon.com/neptune/. accessed 2025-04-14

    2025

  6. [6]

    Ilya Amburg, Nate Veldt, and Austin Benson. 2020. Clustering in Graphs and Hypergraphs with Categorical Edge Labels. InProceedings of the Web Conference(Taipei, Taiwan)(WWW ’20). Association for Computing Machinery, New York, NY, USA, 706–717. https://doi.org/10.1145/ 3366423.3380152

    arXiv 2020

  7. [8]

    Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reut- ter, and Domagoj Vrgoˇ c. 2017. Foundations of Modern Query Languages for Graph Databases.ACM Comput. Surv.50, 5, Article 68 (Sept. 2017), 40 pages. https://doi.org/10.1145/3104031

    doi:10.1145/3104031 2017

  8. [9]

    Renzo Angles and Claudio Gutierrez. 2008. Survey of Graph Database Models.ACM Comput. Surv.40, 1, Article 1 (Feb. 2008), 39 pages. https: //doi.org/10.1145/1322432.1322433

    arXiv 2008

Show all 202 references
  1. [10]

    Renzo Angles and Claudio Gutierrez. 2018. An Introduction to Graph Data Management. InGraph Data Management: Fundamental Issues and Re- cent Developments, George Fletcher, Jan Hidders, and Josep Lluís Larriba- Pey (Eds.). Springer, Berlin, Germany, 1–32. https://doi.org/10.100...

    2018

  2. [11]

    Apache Software Foundation. 2018. Apache Mormotta. http:// marmotta.apache.org/. accessed 2025-04-14

    2018

  3. [12]

    Apache Software Foundation. 2025. Apache Jena TBD. https://jena. apache.org/documentation/tdb/index.html. accessed 2025-04-14

    2025

  4. [13]

    ArangoDB Inc. 2025. ArangoDB. https://docs.arangodb.com/3.11/ concepts/data-models/. accessed 2025-04-14

    2025

  5. [14]

    Armstrong, Vamsi Ponnekanti, Dhruba Borthakur, and Mark Callaghan

    Timothy G. Armstrong, Vamsi Ponnekanti, Dhruba Borthakur, and Mark Callaghan. 2013. LinkBench: A Database Benchmark Based on the Facebook Social Graph. InProceedings of the International Conference on Management of Data(New York, NY, USA)(SIGMOD ’13). Association for Computing...

    2013

  6. [15]

    Siddhant Arora. 2020. A Survey on Graph Neural Networks for Knowl- edge Graph Completion. https://doi.org/10.48550/arXiv.2007.12374 arXiv:2007.12374 [cs.CL]

    2020 doi arXiv

  7. [16]

    Devanshu Arya, Deepak K Gupta, Stevan Rudinac, and Marcel Wor- ring. 2020. HyperSAGE: Generalizing Inductive Representation Learn- ing on Hypergraphs. https://doi.org/10.48550/arXiv.2010.04558 arXiv:2010.04558 [cs.LG]

    2020 doi arXiv

  8. [17]

    Atlan. 2024. Data Consistency 101: Definitions, Types, and Use Cases. https://atlan.com/data-consistency-101. accessed: 2025-04-11

    2024

  9. [18]

    Song Bai, Feihu Zhang, and Philip HS Torr. 2021. Hypergraph Convolu- tion and Hypergraph Attention.Pattern Recognition110, Article 107637 (Feb. 2021), 8 pages. https://doi.org/10.1016/j.patcog.2020.107637

    2021

  10. [19]

    Sumita Barahmand and Shahram Ghandeharizadeh. 2013. BG: A Bench- mark to Evaluate Interactive Social Networking Actions. InProceedings of the 6th Biennial Conference on Innovative Data Systems Research(Asilomar, CA, USA)(CIDR ’13). 12 pages. https://www.cidrdb.org/cidr2013/ Pa...

    2013

  11. [20]

    Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, and Torsten Hoefler. 2023. Cached Operator Reordering: A Unified View for Fast GNN Training. https://doi.org/10.48550/arXiv. 2308.12093 arXiv:2308.12093 [cs.LG]

    2023 doi arXiv

  12. [21]

    Lydia Benkaidali, Francois André, Boubekeur Maouche, Pridi Siregar, Mohamed Benyettou, François Maurel, and Michel Petitjean. 2014. Com- puting Cavities, Channels, Pores and Pockets in Proteins from Non- Spherical Ligands Models.Bioinformatics30, 6 (March 2014), 792–800. https...

    2014 doi

  13. [22]

    Aaron Bernstein, Sebastian Forster, and Monika Henzinger. 2021. A Deamortization Approach for Dynamic Spanner and Dynamic Maximal Matching.ACM Trans. Algorithms17, 4, Article 29 (Oct. 2021), 51 pages. https://doi.org/10.1145/3469833

    2021 doi

  14. [23]

    Maciej Besta, Armon Carigiet, Kacper Janda, Zur Vonarburg-Shmaria, Lukas Gianinazzi, and Torsten Hoefler. 2020. High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality. In Proceedings of the International Conference for High Performance Comp...

    2020 arXiv

  15. [24]

    Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi, Nils Blach, Piotr Nyczyk, Hubert Niewiadomski, and Torsten Hoefler. 2023. HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers. InProceedings of the Second Learning on Graphs Confer- ence ...

    2023

  16. [25]

    Maciej Besta, Marc Fischer, Tal Ben-Nun, Dimitri Stanojevic, Johannes De Fine Licht, and Torsten Hoefler. 2020. Substream-Centric Maximum Matchings on FPGA.ACM Trans. Reconfigurable Technol. Syst.13, 2, Article 8 (April 2020), 33 pages. https://doi.org/10.1145/3377871

    2020 doi

  17. [26]

    Maciej Besta, Marc Fischer, Vasiliki Kalavri, Michael Kapralov, and Torsten Hoefler. 2023. Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems.IEEE Transactions on Parallel and Distributed Systems34, 6 (June 2023), 1860–1876. https://doi.org/10. 1...

    2023

  18. [28]

    Maciej Besta, Robert Gerstenberger, Patrick Iff, Pournima Sonawane, Juan Gómez Luna, Raghavendra Kanakagiri, Rui Min, Grzegorz Kwa´ sniewski, Onur Mutlu, Torsten Hoefler, Raja Appuswamy, and Aidan O Mahony. 2024. Hardware Acceleration for Knowledge Graph Processing: Challenges...

    2024 doi arXiv

  19. [29]

    Maciej Besta, Robert Gerstenberger, Emanuel Peter, Marc Fischer, Michał Podstawski, Claude Barthels, Gustavo Alonso, and Torsten Hoefler

  20. [30]

    Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwa´ sniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, and Torsten Hoefler. 2022. Motif Prediction with Graph Neural Networks. InProceedings of the 28th ACM SI...

    2022

  21. [31]

    Maciej Besta and Torsten Hoefler. 2015. Accelerating Irregular Com- putations with Hardware Transactional Memory and Active Messages. InProceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing(Portland, OR, USA)(HPDC ’15). As- soci...

    2015

  22. [32]

    Maciej Besta and Torsten Hoefler. 2018. Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations. https: //doi.org/10.48550/arXiv.1806.01799 arXiv:1806.01799 [cs.DS]

    2018 doi arXiv

  23. [33]

    Maciej Besta and Torsten Hoefler. 2024. Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence46, 5 (2024), 2584–2606. https: //doi.org/10.1109/TPAMI.2023.3303431

    2024

  24. [34]

    Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michał Podstawski, Tiancheng Chen, and Torsten Hoefler. 2022. Neural Graph Databases. InProceedings of the First Learning on Graphs Confer- ence (LoG ’22)(Virtual Event)(Proceedings of Machine Learning Re...

    2022

  25. [35]

    Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwa´ sniewski, Rachata Ausavarungnirun, Jakub Beránek, Konstantinos Kanellopoulos, Kacper Janda, Zur Vonarburg-Shmaria, Lukas Gianinazzi, Ioana Stefan, Juan Gómez Luna, Jakub Golinowski, Marcin Copik, Lukas Kapp- Schwoerer, Salvat...

    2021

  26. [37]

    Maciej Besta, Michał Podstawski, Linus Groner, Edgar Solomonik, and Torsten Hoefler. 2017. To Push or To Pull: On Reducing Com- munication and Synchronization in Graph Computations. InProceed- ings of the 26th International Symposium on High-Performance Parallel and Distribute...

    2017

  27. [38]

    Maciej Besta, Pawel Renc, Robert Gerstenberger, Paolo Sylos Labini, Alexandros Ziogas, Tiancheng Chen, Lukas Gianinazzi, Florian Scheidl, Kalman Szenes, Armon Carigiet, Patrick Iff, Grzegorz Kwa´ sniewski, Raghavendra Kanakagiri, Chio Ge, Sammy Jaeger, Jarosław W ˛ as, Flavio ...

    2023

  28. [39]

    Maciej Besta, Florian Scheidl, Lukas Gianinazzi, Grzegorz Kwa´ sniewski, Shachar Klaiman, Jürgen Müller, and Torsten Hoefler. 2024. Demystify- ing Higher-Order Graph Neural Networks. https://doi.org/10.48550/ arXiv.2406.12841 arXiv:2406.12841 [cs.LG]

    2024 doi

  29. [40]

    Maciej Besta, Dimitri Stanojevic, Tijana Zivic, Jagpreet Singh, Maurice Hoerold, and Torsten Hoefler. 2018. Log(Graph): A Near-Optimal High- Performance Graph Representation. InProceedings of the 27th International Conference on Parallel Architectures and Compilation Technique...

    2018

  30. [41]

    Maciej Besta, Zur Vonarburg-Shmaria, Yannick Schaffner, Leonardo Schwarz, Grzegorz Kwa´ sniewski, Lukas Gianinazzi, Jakub Beranek, Kacper Janda, Tobias Holenstein, Sebastian Leisinger, Peter Tatkowski, Esref Ozdemir, Adrian Balla, Marcin Copik, Philipp Lindenberger, Marek Koni...

    2021

  31. [42]

    Maciej Besta, Simon Weber, Lukas Gianinazzi, Robert Gerstenberger, An- drei Ivanov, Yishai Oltchik, and Torsten Hoefler. 2019. Slim Graph: Prac- tical Lossy Graph Compression for Approximate Graph Processing, Stor- age, and Analytics. InProceedings of the International Confere...

    2019

  32. [43]

    Harrington, and Michael T

    Christian Bick, Elizabeth Gross, Heather A. Harrington, and Michael T. Schaub. 2023. What Are Higher-Order Networks?SIAM Rev.65, 3 (Aug. 2023), 686–731. https://doi.org/10.1137/21m1414024

    2023 doi

  33. [44]

    Blazegraph. 2020. BlazeGraph DB. https://www.blazegraph.com/. accessed 2025-04-14

    2020

  34. [45]

    Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Lio, Guido F Montufar, and Michael Bronstein. 2021. Weisfeiler and Lehman Go Cellular: CW Networks. InProceedings of the Thirty-Fifth Annual Conference on Neural Information Processing Systems (NeurIPS ’21)(Vir...

    2021

  35. [46]

    Montufar, Pietro Lio, and Michael Bronstein

    Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montufar, Pietro Lio, and Michael Bronstein. 2021. Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. InPro- ceedings of the 38th International Conference on Machine Learning (ICML ’21...

    2021

  36. [47]

    2018.Querying Graphs

    Angela Bonifati, George Fletcher, Hannes Voigt, and Nikolay Yakovets. 2018.Querying Graphs. Springer, Berlin/Heidelberg. https://doi.org/ 10.1007/978-3-031-01864-0

    2018 doi

  37. [48]

    Bronstein

    Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein. 2022. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting.IEEE Transactions on Pattern Analysis and Machine Intelligence45, 1 (Jan. 2022), 657–668. https://doi.org/10. 1...

    2022

  38. [49]

    Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst

    Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going Beyond Euclidean Data.IEEE Signal Processing Magazine34, 4 (July 2017), 18–42. https://doi.org/10.1109/MSP .2017.2693418

    2017

  39. [50]

    Eric Bunch, Qian You, Glenn Fung, and Vikas Singh. 2020. Simplicial 2-Complex Convolutional Neural Nets. InProceedings of the Workshop TDA & Beyond(Virtual Event). OpenReview, Amherst, MA, USA, 5 pages. https://openreview.net/forum?id=TLbnsKrt6J-

    2020

  40. [51]

    Mihai Capot˘ a, Tim Hegeman, Alexandru Iosup, Arnau Prat-Pérez, Orri Erling, and Peter Boncz. 2015. Graphalytics: A Big Data Benchmark for Graph-Processing Platforms. InProceedings of the International Workshop on Graph Data Management Experiences & Systems(Melbourne, Australi...

    2015

  41. [52]

    Timoteo Carletti, Federico Battiston, Giulia Cencetti, and Duccio Fanelli

  42. [53]

    Cayley. 2021. CayleyGraph. https://cayley.io/. accessed 2025-04-14

    2021

  43. [54]

    Deepayan Chakrabarti and Christos Faloutsos. 2006. Graph Mining: Laws, Generators, and Algorithms.ACM Comput. Surv.38, 1 (June 2006), 69 pages. https://doi.org/10.1145/1132952.1132954

    2006

  44. [55]

    Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy. 2022. Machine Learning on Graphs: A Model and Com- prehensive Taxonomy.Journal of Machine Learning Research23, Article 89 (2022), 64 pages. http://jmlr.org/papers/v23/20-852.html

    2022

  45. [56]

    Hongzhi Chen, Changji Li, Juncheng Fang, Chenghuan Huang, James Cheng, Jian Zhang, Yifan Hou, and Xiao Yan. 2019. Grasper: A High Performance Distributed System for OLAP on Property Graphs. In Proceedings of the Symposium on Cloud Computing(Santa Cruz, CA, USA) (SoCC ’19). Ass...

    2019

  46. [57]

    Xuexin Chen, Ruichu Cai, Zhengting Huang, Zijian Li, Jie Zheng, and Min Wu. 2025. Interpretable High-Order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers.Briefings in Bioinformatics26, 2, Article bbaf142 (April 2025), 12 pages. https: //doi....

    2025 doi

  47. [58]

    Zhe Chen, Yuehan Wang, Bin Zhao, Jing Cheng, Xin Zhao, and Zongtao Duan. 2020. Knowledge Graph Completion: A Review.IEEE Access8 (2020), 192435–192456. https://doi.org/10.1109/ACCESS.2020.3030076

    2020

  48. [59]

    Uthsav Chitra and Benjamin Raphael. 2019. Random Walks on Hy- pergraphs with Edge-Dependent Vertex Weights. InProceedings of the 36th International Conference on Machine Learning (ICML ’21)(Long Beach, CA, USA)(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaud...

    2019

  49. [60]

    Sutanay Choudhury, Khushbu Agarwal, Sumit Purohit, Baichuan Zhang, Meg Pirrung, Will Smith, and Mathew Thomas. 2017. NOUS: Con- struction and Querying of Dynamic Knowledge Graphs. InProceed- ings of the 33rd International Conference on Data Engineering(San Diego, CA, USA)(ICDE...

    2017 doi

  50. [61]

    Jaewan Chun, Geon Lee, Kijung Shin, and Jinhong Jung. 2024. Random Walk with Restart on Hypergraphs: Fast Computation and an Applica- tion to Anomaly Detection.Data Mining and Knowledge Discovery38, 3 (May 2024), 1222–1257. https://doi.org/10.1007/s10618-023-00995-9

    2024 doi

  51. [62]

    Marek Ciglan, Alex Averbuch, and Ladialav Hluchy. 2012. Benchmarking Traversal Operations over Graph Databases. InProceedings of the 28th International Conference on Data Engineering Workshops(Arlington, VA, USA)(ICDEW ’12). IEEE Press, Washington, DC, USA, 186–189. https: //d...

    2012 doi

  52. [63]

    2006.Mining Graph Data

    Diane J Cook and Lawrence B Holder (Eds.). 2006.Mining Graph Data. John Wiley & Sons, Hoboken, NJ, USA

    2006

  53. [64]

    2014.RDF 1.1 Concepts and Abstract Syntax

    Richard Cyganiak, David Wood, and Markus Lanthaler. 2014.RDF 1.1 Concepts and Abstract Syntax. World Wide Web Consortium

    2014

  54. [65]

    Xiong, and Wenzhong Guo

    Yuanfei Dai, Shiping Wang, Neal N. Xiong, and Wenzhong Guo. 2020. A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks.Electronics9, 5, Article 750 (May 2020), 29 pages. https://doi.org/10.3390/electronics9050750

    2020 doi

  55. [66]

    DataStax Inc. 2025. DSE Graph (DataStax). https://www.datastax.com/. accessed 2025-04-14

    2025

  56. [67]

    Ali Davoudian, Liu Chen, and Mengchi Liu. 2018. A Survey on NoSQL Stores.ACM Comput. Surv.51, 2, Article 40 (April 2018), 43 pages. https://doi.org/10.1145/3158661

    2018 doi

  57. [68]

    Alin Deutsch, Yu Xu, Mingxi Wu, and Victor E. Lee. 2020. Aggregation Support for Modern Graph Analytics in TigerGraph. InProceedings of the International Conference on Management of Data(Portland, OR, USA) (SIGMOD ’20). Association for Computing Machinery, New York, NY, USA, 3...

    2020

  58. [69]

    Dgraph Labs, Inc. 2018. DGraph. https://docs.hypermode.com/ dgraph/overview. accessed 2025-04-14

    2018

  59. [70]

    Salvatore Di Girolamo, Daniele De Sensi, Konstantin Taranov, Milos Malesevic, Maciej Besta, Timo Schneider, Severin Kistler, and Torsten Hoefler. 2022. Building Blocks for Network-Accelerated Distributed File Systems. InProceedings of the International Conference for High Perf...

    2022 arXiv

  60. [71]

    Salvatore Di Girolamo, Konstantin Taranov, Andreas Kurth, Michael Schaffner, Timo Schneider, Jakub Beránek, Maciej Besta, Luca Benini, Duncan Roweth, and Torsten Hoefler. 2019. Network-Accelerated Non- Contiguous Memory Transfers. InProceedings of the International Con- ferenc...

    2019 doi

  61. [72]

    David Dominguez-Sal, Peter Urbón-Bayes, Aleix Giménez-Vanó, Sergio Gómez-Villamor, Norbert Martínez-Bazan, and Josep-Lluis Larriba-Pey

  62. [73]

    Ran Duan, Haoqing He, and Tianyi Zhang. 2019. Dynamic Edge Color- ing with Improved Approximation. InProceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms(San Diego, CA, USA) (SODA ’19). Society for Industrial and Applied Mathematics, USA, 1937–

    2019

  63. [74]

    Hill, Robert Escriva, and Emin Gün Sirer

    Ayush Dubey, Greg D. Hill, Robert Escriva, and Emin Gün Sirer. 2016. Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps.Proc. VLDB Endow.9, 11 (July 2016), 852–863. M. Besta et al. https://doi.org/10.14778/2983200.2983202

    2016

  64. [75]

    Anton Eriksson, Daniel Edler, Alexis Rojas, Manlio de Domenico, and Martin Rosvall. 2021. How Choosing Random-Walk Model and Network Representation Matters for Flow-Based Community Detection in Hyper- graphs.Communications Physics4, 1, Article 133 (June 2021), 12 pages. https:...

    2021 doi

  65. [76]

    Orri Erling, Alex Averbuch, Josep Larriba-Pey, Hassan Chafi, Andrey Gubichev, Arnau Prat, Minh-Duc Pham, and Peter Boncz. 2015. The LDBC Social Network Benchmark: Interactive Workload. InProceedings of the International Conference on Management of Data(Melbourne, Australia) (S...

    2015

  66. [77]

    FactNexus. 2025. GraphBase. https://graphbase.ai/. accessed 2025-04- 14

    2025

  67. [78]

    Haoyi Fan, Fengbin Zhang, Yuxuan Wei, Zuoyong Li, Changqing Zou, Yue Gao, and Qionghai Dai. 2022. Heterogeneous Hypergraph Vari- ational Autoencoder for Link Prediction.IEEE Transactions on Pat- tern Analysis and Machine Intelligence44, 8 (Aug. 2022), 4125–4138. https://doi.or...

    2022

  68. [79]

    Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao

  69. [80]

    Matthias Fey, Jan-Gin Yuen, and Frank Weichert. 2020. Hierarchical Inter- Message Passing for Learning on Molecular Graphs. InProceedings of the Workshop on Graph Representation Learning and Beyond(Virtual Event) (GRL+ ’20). 6 pages. https://grlplus.github.io/papers/45.pdf

    2020

  70. [81]

    Nadime Francis, Alastair Green, Paolo Guagliardo, Leonid Libkin, To- bias Lindaaker, Victor Marsault, Stefan Plantikow, Mats Rydberg, Petra Selmer, and Andrés Taylor. 2018. Cypher: An Evolving Query Lan- guage for Property Graphs. InProceedings of the International Confer- enc...

    2018

  71. [82]

    Franz Inc. 2025. AllegroGraph. https://allegrograph.com/products/ allegrograph/. accessed 2025-04-14

    2025

  72. [83]

    Santhosh Kumar Gajendran. 2012. A Survey on NoSQL Databases

    2012

  73. [84]

    Robert Gerstenberger, Maciej Besta, and Torsten Hoefler. 2013. Enabling Highly-Scalable Remote Memory Access Programming with MPI-3 One Sided. InProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis(Denver, CO, USA)(SC ’13)...

    2013

  74. [85]

    Lukas Gianinazzi, Maciej Besta, Yannick Schaffner, and Torsten Hoefler

  75. [86]

    Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Ma- ciej Besta, and Torsten Hoefler. 2022. Learning Combinatorial Node Labeling Algorithms. https://doi.org/10.48550/arXiv.2106.03594 arXiv:2106.03594 [cs.LG]

    2022 doi arXiv

  76. [87]

    Lukas Gianinazzi, Pavel Kalvoda, Alessandro De Palma, Maciej Besta, and Torsten Hoefler. 2018. Communication-Avoiding Parallel Minimum Cuts and Connected Components.SIGPLAN Not.53, 1 (Feb. 2018), 219–232. https://doi.org/10.1145/3200691.3178504

    2018

  77. [88]

    Lorenzo Giusti, Claudio Battiloro, Lucia Testa, Paolo Di Lorenzo, Stefa- nia Sardellitti, and Sergio Barbarossa. 2023. Cell Attention Networks. In Proceedings of the International Joint Conference on Neural Networks(Queens- land, Australia)(IJCNN ’23). IEEE Press, Washington, ...

    2023

  78. [89]

    Christopher Wei Jin Goh, Cristian Bodnar, and Pietro Lio. 2022. Simpli- cial Attention Networks. InProceedings of the Workshop on Geometrical and Topological Representation Learning(Virtual Event)(GTRL ’22). OpenRe- view, Amherst, MA, USA, 7 pages

    2022

  79. [90]

    Google Inc. 2025. Spanner Graph overview. https://cloud.google.com/ spanner/docs/graph/overview. accessed: 2025-04-15

    2025

  80. [91]

    Oded Green, James Fox, Jeff Young, Jun Shirako, and David Bader

  81. [92]

    Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi. 2020. Cell Complex Neural Networks. InProceedings of the Workshop TDA & Beyond(Virtual Event). OpenReview, Amherst, MA, USA, 13 pages. https://openreview. net/forum?id=6Tq18ySFpGU

    2020

  82. [93]

    Dey, Soham Mukherjee, Shreyas N

    Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, and Michael T. Schaub. 2023. Topological Deep Learning: G...

    2023 doi arXiv

  83. [94]

    Hamilton, Rex Ying, and Jure Leskovec

    William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation Learning on Graphs: Methods and Applications.Bulletin of the Technical Committee on Data Engineering40, 3 (Sept. 2017), 52–74. http://sites. computer.org/debull/A17sept/p52.pdf

    2017

  84. [95]

    Jing Han, E Haihong, Guan Le, and Jian Du. 2011. Survey on NoSQL Database. InProceedings of the 6th International Conference on Pervasive Computing and Applications(Port Elizabeth, South Africa)(ICPCA ’11). IEEE Press, Washington, DC, USA, 363–366. https://doi.org/10.1109/ ICP...

    2011

  85. [96]

    Steve Harris, Nick Lamb, and Nigel Shadbolt. 2009. 4store: The Design and Implementation of a Clustered RDF Store. InProceedings of the 5th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS ’09)(Washington, DC, USA)(Workshop Proceedings, Vol. 517), A...

    2009

  86. [97]

    Aksoy, Cheong Hee Park, and Haesun Park

    Koby Hayashi, Sinan G. Aksoy, Cheong Hee Park, and Haesun Park

  87. [99]

    InProceedings of the IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms(Denver, CO, USA)(IA3 ’19)

    Performance Impact of Memory Channels on Sparse and Irregular Algorithms. InProceedings of the IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms(Denver, CO, USA)(IA3 ’19). IEEE Press, Washington, DC, USA, 67–70. https://doi.org/10.1109/ IA349570.2019.00016

  88. [100]

    Tamás Horváth, Thomas Gärtner, and Stefan Wrobel. 2004. Cyclic Pat- tern Kernels for Predictive Graph Mining. InProceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Min- ing(Seattle, WA, USA)(KDD ’04). Association for Computing Machin- er...

    2004 doi

  89. [101]

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2020. Strategies for Pre-Training Graph Neural Networks. InProceedings of the Eighth International Conference on Learning Representations(Virtual Event)(ICLR ’20). OpenReview, Amhe...

    2020

  90. [102]

    Alexandru Iosup, Tim Hegeman, Wing Lung Ngai, Stijn Heldens, Ar- nau Prat-Pérez, Thomas Manhardto, Hassan Chafio, Mihai Capot˘ a, Narayanan Sundaram, Michael Anderson, Ilie Gabriel T˘ anase, Yin- glong Xia, Lifeng Nai, and Peter Boncz. 2016. LDBC Graphalytics: A Benchmark for ...

    2016

  91. [103]

    Irwin, Khanh G

    John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchu- luun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle. 2020. ZINC20 — A Free Ultralarge- Scale Chemical Database for Ligand Discovery.Journal of Chemi- cal Information...

    2020 doi

  92. [104]

    JanusGraph Contributors. 2024. JanusGraph: An Open-Source, Dis- tributed Graph Database. https://github.com/JanusGraph/janusgraph. accessed 2025-04-11

    2024

  93. [105]

    Chuntao Jiang, Frans Coenen, and Michele Zito. 2013. A Survey of Fre- quent Subgraph Mining Algorithms.The Knowledge Engineering Review 28, 1 (Nov. 2013), 75–105. https://doi.org/10.1017/S0269888912000331

    2013 doi

  94. [106]

    InProceed- ings of the 29th ACM International Conference on Information & Knowledge Management(Virtual Event)(CIKM ’20)

    Hypergraph Random Walks, Laplacians, and Clustering. InProceed- ings of the 29th ACM International Conference on Information & Knowledge Management(Virtual Event)(CIKM ’20). Association for Computing Machinery, New York, NY, USA, 495–504. https://doi.org/10.1145/ 3340531.3412034

  95. [107]

    Salim Jouili and Valentin Vansteenberghe. 2013. An Empirical Compari- son of Graph Databases. InProceedings of the International Conference on Higher-Order Graph Databases Social Computing(Washington, DC, USA)(SocialCom ’13). IEEE Press, Washington, DC, USA, 708–715. https://d...

    2013 doi

  96. [108]

    Johan Hoffman, Johan Jansson, and Niclas Jansson. 2016. FEniCS-HPC: Automated Predictive High-Performance Finite Element Computing with Applications in Aerodynamics. InProceedings of the 11th Interna- tional Conference on Parallel Processing and Applied Mathematics (PP AM ’15)...

    2016 doi

  97. [109]

    Matthias Jurisch and Bodo Igler. 2018. RDF2Vec-Based Classification of Ontology Alignment Changes. InProceedings of the Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS ’18) (Heraklion, Greece)(Workshop Proceedings, Vol. 2106), Michael Cochez, T...

    2018

  98. [110]

    Kang, Hanghang Tong, Jimeng Sun, Ching-Yung Lin, and Christos Faloutsos

    U. Kang, Hanghang Tong, Jimeng Sun, Ching-Yung Lin, and Christos Faloutsos. 2012. Gbase: An Efficient Analysis Platform for Large Graphs. The VLDB Journal21, 5 (Oct. 2012), 637–650. https://doi.org/10.1007/ s00778-012-0283-9

    2012

  99. [111]

    Chathura Kankanamge, Siddhartha Sahu, Amine Mhedbhi, Jeremy Chen, and Semih Salihoglu. 2017. Graphflow: An Active Graph Database. In Proceedings of the International Conference on Management of Data(Chicago, Illinois, USA)(SIGMOD ’17). Association for Computing Machinery, New ...

    2017 doi

  100. [112]

    Kipf and Max Welling

    Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. InProceedings of the 5th Interna- tional Conference on Learning Representations(Toulon, France)(ICLR ’17). OpenReview, Amherst, MA, USA, 14 pages. https://openreview.net/ fo...

    2017

  101. [113]

    Vijay Kumar and Anjan Babu. 2015. Domain Suitable Graph Database Se- lection: A Preliminary Report. InProceedings on the 3rd International Con- ference on Advances in Engineering Sciences & Applied Mathematics(London, UK)(ICAESAM ’15). International Institute of Engineers, Mum...

    2015

  102. [114]

    Rohit kumar Kaliyar. 2015. Graph Databases: A Survey. InProceedings of the International Conference on Computing, Communication & Automation (Greater Noida, UP , India)(ICCCA ’15), Abhishek Swaropp and Vishnu Sharma (Eds.). IEEE Press, Washington, DC, USA, 785–790. https: //do...

    2015

  103. [115]

    Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction Tree Variational Autoencoder for Molecular Graph Generation. InProceed- ings of the 35th International Conference on Machine Learning (ICML ’18) (Stockholm, Sweden)(Proceedings of Machine Learning Research, Vol. ...

    2018

  104. [116]

    Jungmin Lee, Minsu Cho, and Kyoung Mu Lee. 2011. Hyper-Graph Matching Via Reweighted Random Walks. InProceedings of the Conference on Computer Vision and Pattern Recognition(Colorado Springs, CO, USA) (CVPR ’11). IEEE Press, Washington, DC, USA, 1633–1640. https://doi. org/10....

    2011

  105. [117]

    Martin Junghanns, André Petermann, Martin Neumann, and Erhard Rahm. 2017. Management and Analysis of Big Graph Data: Current Systems and Open Challenges. InHandbook of Big Data Technologies, Albert Y. Zomaya and Sherif Sakr (Eds.). Springer, Berlin/Heidelberg, Germany, 457–505...

    2017 doi

  106. [118]

    2017.An Evaluation Methodology and Experimental Comparison of Graph Databases

    Matteo Lissandrini, Martin Brugnara, and Yannis Velegrakis. 2017.An Evaluation Methodology and Experimental Comparison of Graph Databases. Technical Report. University of Trento

    2017

  107. [119]

    Matteo Lissandrini, Martin Brugnara, and Yannis Velegrakis. 2018. Beyond Macrobenchmarks: Microbenchmark-Based Graph Database Evaluation.Proc. VLDB Endow.12, 4 (Dec. 2018), 390–403. https: //doi.org/10.14778/3297753.3297759

    2018

  108. [120]

    Linyuan Lu and Xing Peng. 2013. High-Order Random Walks and Generalized Laplacians on Hypergraphs.Internet Mathematics9, 1 (Jan. 2013), 3–32. https://doi.org/10.1080/15427951.2012.678151

    2013

  109. [121]

    Qi Luo, Zhenzhen Xie, Yu Liu, Dongxiao Yu, Xiuzhen Cheng, Xuemin Lin, and Xiaohua Jia. 2024. Sampling Hypergraphs via Joint Unbiased Random Walk.World Wide Web27, 2, Article 15 (Feb. 2024), 24 pages. https://doi.org/10.1007/s11280-024-01253-8

    2024 doi

  110. [122]

    Zhitao Mao, Ruoyu Wang, Haoran Li, Yixin Huang, Qiang Zhang, Xi- aoping Liao, and Hongwu Ma. 2022. ERMer: A Serverless Platform for Navigating, Analyzing, and Visualizing Escherichia coli Regulatory Landscape Through Graph Database.Nucleic Acids Research50, W1 (April 2022), W298–W304

    2022

  111. [123]

    Ángel Águila Lorente, Victor Muntés- Mulero, David Dominguez-Sal, Sergio Gómez-Villamor, and Josep-L

    Norbert Martínez-Bazan, M. Ángel Águila Lorente, Victor Muntés- Mulero, David Dominguez-Sal, Sergio Gómez-Villamor, and Josep-L. Larriba-Pey. 2012. Efficient Graph Management Based On Bitmap Indices. InProceedings of the 16th International Database Engineering & Applications S...

    2012

  112. [124]

    Ora Lassila and Ralph R. Swick. 1998.Resource Description Framework (RDF) Model and Syntax Specification. World Wide Web Consortium

    1998

  113. [125]

    Memgraph Ltd. 2025. Memgraph. https://memgraph.com/. accessed 2025-04-14

    2025

  114. [126]

    Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Com- pletion.Proceedings of the AAAI Conference on Artificial Intelligence29, 1 (Feb. 2015), 2181–2187. https://doi.org/10.1609/aaai.v29i1.9491

    2015 doi

  115. [127]

    Justin J. Miller. 2013. Graph Database Applications and Concepts with Neo4j. InProceedings of the Southern Association for Information Systems Conference(Savannah, GA, USA)(SAIS ’13). Association for Information Systems, Atlanta, GA, USA. https://aisel.aisnet.org/sais2013/24/

    2013

  116. [128]

    Ali Mirsoleimani, Aske Plaat, Jaap Van Den Herik, and Jos Vermaseren

    S. Ali Mirsoleimani, Aske Plaat, Jaap Van Den Herik, and Jos Vermaseren

  117. [129]

    Modoni, Marco Sacco, and Walter Terkaj

    Gianfranco E. Modoni, Marco Sacco, and Walter Terkaj. 2014. A Survey of RDF Store Solutions. InProccedings of the International Conference on Engineering, Technology and Innovation(Bergamo, Italy)(ICE ’14). IEEE Press, Washington, DC, USA, 1–7. https://doi.org/10.1109/ICE.2014...

    2014 doi

  118. [130]

    2024.Data Consistency — MongoDB Manual

    MongoDB, Inc. 2024.Data Consistency — MongoDB Manual. MongoDB. accessed: 2025-04-11

    2024

  119. [131]

    Christopher Morris, Gaurav Rattan, and Petra Mutzel. 2020. Weisfeiler and Leman Go Sparse: Towards Scalable Higher-Order Graph Embed- dings. InProceedings of the Thirty-Fourth Annual Conference on Neural Information Processing Systems (NeurIPS ’20)(Virtual Event)(Advances in N...

    2020

  120. [132]

    Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

    Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks.Proceedings of the AAAI Conference on Artificial Intelligence33, 01 (July 2019), 4...

    2019 doi

  121. [133]

    Robert Campbell McColl, David Ediger, Jason Poovey, Dan Camp- bell, and David A. Bader. 2014. A Performance Evaluation of Open Source Graph Databases. InProceedings of the First Workshop on Paral- lel Programming for Analytics Applications(Orlando, FL, USA)(PP AA ’14). Associa...

    2014

  122. [134]

    Raffaella Mulas, Christian Kuehn, Tobias Böhle, and Jürgen Jost. 2022. Random Walks and Laplacians on Hypergraphs: When Do They Match? Discrete Applied Mathematics317 (Aug. 2022), 26–41. https://doi.org/10. 1016/j.dam.2022.04.009

    2022

  123. [135]

    Microsoft. 2025. Azure Cosmos DB. https://azure.microsoft.com/en- us/products/cosmos-db/. accessed 2025-04-14

    2025

  124. [136]

    Networked Planet Limited. 2019. BrightstarDB. http://brightstardb. com/. accessed 2025-04-14

    2019

  125. [137]

    Ya-Wei Niu, Cun-Quan Qu, Guang-Hui Wang, and Gui-Ying Yan. 2019. RWHMDA: Random Walk on Hypergraph for Microbe-Disease Asso- ciation Prediction.Frontiers in Microbiology10, Article 1578 (July 2019), 10 pages. https://doi.org/10.3389/fmicb.2019.01578

    2019

  126. [138]

    NVIDIA. 2025. cuGraph. https://github.com/rapidsai/cugraph. ac- cessed: 2025-04-15

    2025

  127. [139]

    Ontotext. 2025. GraphDB. https://www.ontotext.com/products/ graphdb/. accessed 2025-04-14

    2025

  128. [140]

    OpenLink. 2024. Virtuoso. https://virtuoso.openlinksw.com/. accessed 2025-04-14

    2024

  129. [141]

    Oracle. 2025. Oracle Spatial and Graph. https://www.oracle.com/ database/spatial-database/. accessed 2025-04-14

    2025

  130. [142]

    Nikolaos Papailiou, Ioannis Konstantinou, Dimitrios Tsoumakos, and Nectarios Koziris. 2012. H2RDF: Adaptive Query Processing on RDF Data in the Cloud. InProceedings of the 21st International Conference on World Wide Web(Lyon, France)(WWW ’12 Companion). Association for Computi...

    2012

  131. [143]

    Abubakr Muhammad and Ali Jadbabaie. 2008. Dynamic Coverage Verification in Mobile Sensor Networks Via Switched Higher Order Laplacians. InRobotics: Science and Systems III, Wolfram Burgard, Oliver Brock, and Cyrill Stachniss (Eds.). MIT Press, Cambridge, MA, USA. https://doi.o...

    2008 doi

  132. [144]

    Jaroslav Pokorn `y. 2015. Graph Databases: Their Power and Limitations. InProceedings of the IFIP International Conference on Computer Informa- tion Systems and Industrial Management (CISIM ’15)(Warsaw, Poland) (Lecture Notes in Computer Science (LNISA), Vol. 9339), Khalid Sae...

    2015 doi

  133. [145]

    Ofer Neiman and Shay Solomon. 2015. Simple Deterministic Algorithms for Fully Dynamic Maximal Matching.ACM Trans. Algorithms12, 1, Article 7 (Nov. 2015), 15 pages. https://doi.org/10.1145/2700206

    2015 doi

  134. [146]

    Profium. 2025. Profium Sense. https://www.profium.com/en/ products/graph-database/. accessed 2025-04-14

    2025

  135. [147]

    Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, and Christopher Morris. 2022. Ordered Subgraph Aggregation Net- works. InProceedings of the Thirty-Sixth Annual Conference on Neu- ral Information Processing Systems (NeurIPS ’22)(New Orleans, LA, USA)(Advances in Neu...

    2022

  136. [148]

    Hongchao Qin, Rong-Hua Li, Guoren Wang, Lu Qin, Yurong Cheng, and Ye Yuan. 2019. Mining Periodic Cliques in Temporal Networks. InProceedings of the 35th International Conference on Data Engineering (Macau, China)(ICDE ’19). IEEE Press, Washington, DC, USA, 1130–

    2019

  137. [149]

    Redis Labs. 2023. RedisGraph. https://redis.io/docs/stack/graph/. accessed 2025-04-14

    2023

  138. [150]

    Rickett, Utz-Uwe Haus, James Maltby, and Kristyn J

    Christopher D. Rickett, Utz-Uwe Haus, James Maltby, and Kristyn J. Maschhoff. 2018. Loading and Querying a Trillion RDF Triples with Cray Graph Engine on the Cray XC. InProceedings of the Cray User Group (Stockholm, Sweden)(CUG ’18). Cray User Group, Bloomington, IN, USA

    2018

  139. [151]

    Petar Ristoski and Heiko Paulheim. 2016. RDF2Vec: RDF Graph Em- beddings for Data Mining. InProceedings of the International Seman- tic Web Conference (ISWC ’16)(Kobe, Japan)(Lecture Notes in Com- puter Science (LNISA), Vol. 9981), Paul Groth, Elena Simperl, Alas- dair Gray, M...

    2016 doi

  140. [152]

    Petar Ristoski, Jessica Rosati, Tommaso Di Noia, Renato De Leone, and Heiko Paulheim. 2019. RDF2Vec: RDF Graph Embeddings and Their Applications.Semantic Web10, 4 (May 2019), 721–752. https: //doi.org/10.3233/SW-180317

    2019 doi

  141. [153]

    Patil, P Kiran, N.P

    N.S. Patil, P Kiran, N.P . Kavya, and K.M. Naresh Patel. 2018. A Survey on Graph Database Management Techniques for Huge Unstructured Data. International Journal of Electrical and Computer Engineering81, 2 (April 2018), 1140–1149. https://doi.org/10.11591/ijece.v8i2.pp1140-1149

    2018 doi

  142. [154]

    Rossi, Nesreen K

    Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, and Yasin Abbasi Yadkori. 2018. HONE: Higher-Order Network Embeddings. https://doi.org/10.48550/arXiv.1801.09303 arXiv:1801.09303 [stat.ML]

    2018 doi arXiv

  143. [155]

    Jan Portisch, Michael Hladik, and Heiko Paulheim. 2020. RDF2Vec Light – A Lightweight Approach for Knowledge Graph Embeddings. InProceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice(Virtual Event)(Workshop Proceedings, Vol. 2721), Ker...

    2020

  144. [156]

    Hermann Schweizer, Maciej Besta, and Torsten Hoefler. 2015. Evaluating the Cost of Atomic Operations on Modern Architectures. InProceedings of the International Conference on Parallel Architecture and Compilation(San Francisco, CA, USA)(P ACT ’15). IEEE Press, Washington, DC, ...

    2015 doi

  145. [157]

    Baoxu Shi and Tim Weninger. 2017. ProjE: Embedding Projection for Knowledge Graph Completion.Proceedings of the AAAI Conference on Artificial Intelligence31, 1 (Feb. 2017), 1236–1242. https://doi.org/10. 1609/aaai.v31i1.10677

    2017

  146. [158]

    Baoxu Shi and Tim Weninger. 2018. Open-World Knowledge Graph Completion.Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (April 2018), 1957–1964. https://doi.org/10.1609/aaai.v32i1.11535

    2018 doi

  147. [159]

    Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo- June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MAS) and Applications. InProceedings of the 24th International Conference on World Wide Web(Florence, Italy)(WWW ’15 Companion). A...

    2015

  148. [160]

    Edgar Solomonik, Maciej Besta, Flavio Vella, and Torsten Hoefler. 2017. Scaling Betweenness Centrality Using Communication-Efficient Sparse Matrix Multiplication. InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis(Den...

    2017

  149. [161]

    Stardog Union. 2025. Stardog. https://www.stardog.com/. accessed 2025-04-14

    2025

  150. [162]

    András Strausz, Flavio Vella, Salvatore Di Girolamo, Maciej Besta, and Torsten Hoefler. 2022. Asynchronous Distributed-Memory Tri- angle Counting and LCC with RMA Caching. InProceedings of the IEEE International Parallel and Distributed Processing Symposium(Lyon, France)(IPDPS...

    2022

  151. [163]

    Xiangguo Sun, Hongzhi Yin, Bo Liu, Hongxu Chen, Jiuxin Cao, Yingxia Shao, and Nguyen Quoc Viet Hung. 2021. Heterogeneous Hypergraph Embedding for Graph Classification. InProceedings of the 14th ACM International Conference on Web Search and Data Mining(Virtual Event) (WSDM ’21...

    2021

  152. [164]

    Ian Robinson, Jim Webber, and Emil Eifrem. 2015. Graph Database Internals. InGraph Databases(2nd ed.). O’Reilly, Sebastopol, CA, USA, Chapter 7, 149–170

    2015

  153. [165]

    Gábor Szárnyas, Arnau Prat-Pérez, Alex Averbuch, József Marton, Mar- cus Paradies, Moritz Kaufmann, Orri Erling, Peter Boncz, Vlad Haprian, and János Benjamin Antal. 2018. An Early Look at the LDBC Social Network Benchmark’s Business Intelligence Workload. InProceedings of the...

    2018

  154. [166]

    Sherif Sakr, Angela Bonifati, Hannes Voigt, Alexandru Iosup, Khaled Am- mar, Renzo Angles, Walid Aref, Marcelo Arenas, Maciej Besta, Peter A. Boncz, Khuzaima Daudjee, Emanuele Della Valle, Stefania Dumbrava, Olaf Hartig, Bernhard Haslhofer, Tim Hegeman, Jan Hidders, Katja Hose...

    2021

  155. [167]

    2013.Getting Started with OrientDB

    Claudio Tesoriero. 2013.Getting Started with OrientDB. Packt Publishing, Birmingham, England

    2013

  156. [168]

    Yuanyuan Tian, Wen Sun, Sui Jun Tong, En Liang Xu, Mir Hamid Pirahesh, and Wei Zhao. 2019. Synergistic Graph and SQL Analytics Inside IBM Db2.Proc. VLDB Endow.12, 12 (Aug. 2019), 1782–1785. https://doi.org/10.14778/3352063.3352065

    2019

  157. [169]

    TigerGraph Inc. 2022. Using the Linked Data Benchmark Council Social Network Benchmark Methodology to Evaluate TigerGraph at 36 Ter- abytes. https://info.tigergraph.com/benchmark-ldbc-36tb. accessed 2025-04-14

    2022

  158. [170]

    TigerGraph Inc. 2025. TigerGraph. https://www.tigergraph.com/. ac- cessed 2025-04-11

    2025

  159. [171]

    Lucian Toader, Alexandru Uta, Ahmed Musaafir, and Alexandru Iosup

  160. [172]

    Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, and Guillaume Bouchard

    Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, and Guillaume Bouchard. 2017. Knowledge Graph Completion via Complex Tensor Factorization.J. Mach. Learn. Res.18, 1 (Jan. 2017), 4735–4772

    2017

  161. [173]

    Ultipa Inc. 2025. Ultipa Graph. https://www.ultipa.com/product/ ultipa-graph. accessed: 2025-04-15

    2025

  162. [174]

    Jacopo Urbani, Sourav Dutta, Sairam Gurajada, and Gerhard Weikum

  163. [175]

    Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, and Yiming Yang. 2020. A Re-Evaluation of Knowledge Graph Completion Methods. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL ’20), Dan Jurafsky, Joyce Chai, Natalie ...

    2020 doi

  164. [176]

    Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, and Xuemin Lin

  165. [177]

    Steer, Dávid Szakállas, Altan Birler, Mingxi Wu, Yuchen Zhang, and Peter Boncz

    Gábor Szárnyas, Jack Waudby, Benjamin A. Steer, Dávid Szakállas, Altan Birler, Mingxi Wu, Yuchen Zhang, and Peter Boncz. 2022. The LDBC Social Network Benchmark: Business Intelligence Workload.Proc. VLDB Endow.16, 4 (Dec. 2022), 877–890. https://doi.org/10.14778/3574245. 3574270

    2022 doi

  166. [178]

    Boris Weisfeiler and Andrey Andreevich Leman. 1968. A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction.Nauchno-Technicheskaya Informatsia9 (1968), 11 pages

    1968

  167. [179]

    Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning (ICML ’19)(Long Beach, CA, USA)(Proceedings of Machine Learning Resea...

    2019

  168. [180]

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021. A Comprehensive Survey on Graph Neural Networks.IEEE Transactions on Neural Networks and Learning Systems32, 1 (Jan. 2021), 4–24. https://doi.org/10.1109/TNNLS.2020. 2978386

    2021 doi

  169. [181]

    Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful Are Graph Neural Networks?. InProceedings of the Seventh International Conference on Learning Representations(New Orleans, LA, USA)(ICLR ’19). OpenReview, Amherst, MA, USA, 17 pages. https: //openrev...

    2019

  170. [182]

    Naganand Yadati. 2020. Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs. InProceedings of the Thirty-Fourth Annual Conference on Neural Information Processing Systems (NeurIPS ’20)(Virtual Event)(Advances in Neural Information Processing Sys- tems,...

    2020

  171. [183]

    InProceedings of the 18th International Symposium on Parallel and Distributed Computing (Amsterdam, Netherlands)(ISPDC ’19)

    Graphless: Toward Serverless Graph Processing. InProceedings of the 18th International Symposium on Parallel and Distributed Computing (Amsterdam, Netherlands)(ISPDC ’19). IEEE Press, Washington, DC, USA, 66–73. https://doi.org/10.1109/ISPDC.2019.00012

  172. [184]

    Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. KG-BERT: BERT for Knowledge Graph Completion. https://doi.org/10.48550/arXiv.1909. 03193 arXiv:1909.03193 [cs.CL]

    2019 doi arXiv

  173. [185]

    Pingpeng Yuan, Pu Liu, Buwen Wu, Hai Jin, Wenya Zhang, and Ling Liu. 2013. TripleBit: A Fast and Compact System for Large Scale RDF Data.Proc. VLDB Endow.6, 7 (May 2013), 517–528. https://doi.org/10. 14778/2536349.2536352

    2013

  174. [186]

    Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2022. Deep Learning on Graphs: A Survey.IEEE Transactions on Knowledge and Data Engineering 34, 1 (Jan. 2022), 249–270. https://doi.org/10.1109/TKDE.2020.2981333

    2022

  175. [187]

    Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, and George Karypis. 2024. GraphStorm: All- in-One Graph Machine Lea...

    2024

  176. [188]

    Vesoft Inc. 2025. NebulaGraph. https://github.com/vesoft-inc/nebula. accessed: 2025-04-15

    2025

  177. [189]

    Xiaowei Zhu, Guanyu Feng, Marco Serafini, Xiaosong Ma, Jiping Yu, Lei Xie, Ashraf Aboulnaga, and Wenguang Chen. 2020. LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans.Proc. VLDB Endow.13, 7 (March 2020), 1020–1034. https: //doi.org/10...

    2020

  178. [190]

    InProceedings of the Twenty-Ninth In- ternational Joint Conference on Artificial Intelligence(Yokohama, Japan) (IJCAI ’20), Christian Bessiere (Ed.)

    GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions. InProceedings of the Twenty-Ninth In- ternational Joint Conference on Artificial Intelligence(Yokohama, Japan) (IJCAI ’20), Christian Bessiere (Ed.). International Joint Conferences on Artific...

    doi

  179. [191]

    Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge Graph Embedding: A Survey of Approaches and Applications.IEEE Transactions on Knowledge and Data Engineering29, 12 (Dec. 2017), 2724–

    2017

  180. [199]

    Maosheng Yang, Elvin Isufi, and Geert Leus. 2022. Simplicial Convo- lutional Neural Networks. InProceedings of the International Conference on Acoustics, Speech and Signal Processing(Singapore)(ICASSP ’22). IEEE Press, Washington, DC, USA, 8847–8851. https://doi.org/10.1109/ I...

    2022

  181. [204]

    Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2021. Graph Neural Networks: A Review of Methods and Applications.AI Open1 (April 2021), 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001

    2021 doi

  182. [206]

    Tamer Özsu, Lei Chen, Xuchuan Shen, Ruizhe Huang, and Dongyan Zhao

    Lei Zou, M. Tamer Özsu, Lei Chen, Xuchuan Shen, Ruizhe Huang, and Dongyan Zhao. 2014. GStore: A Graph-Based SPARQL Query Engine. The VLDB Journal23, 4 (Aug. 2014), 565–590. https://doi.org/10.1007/ s00778-013-0337-7 A Extending the Graph Data Model with Higher-Order A.1 Notati...

    2014

  183. [1141]

    https://doi.org/10.1109/ICDE.2019.00104

    2019

  184. [1945]

    https://dl.acm.org/doi/10.5555/3310435.3310552

  185. [2010]

    InProccedings of the International Conference on Web-Age Information Management (WAIM ’10)(Jiuzhaigou Valley, China) (Lecture Notes in Computer Science (LNISA), Vol

    Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark. InProccedings of the International Conference on Web-Age Information Management (WAIM ’10)(Jiuzhaigou Valley, China) (Lecture Notes in Computer Science (LNISA), Vol. 6185), Heng Tao Shen, Jian P...

    doi

  186. [2015]

    InProceedings of the IEEE 21st International Conference on Parallel and Distributed Systems (Melbourne, Australia)(ICP ADS ’15)

    Scaling Monte Carlo Tree Search on Intel Xeon Phi. InProceedings of the IEEE 21st International Conference on Parallel and Distributed Systems (Melbourne, Australia)(ICP ADS ’15). IEEE Press, Washington, DC, USA, 666–673. https://doi.org/10.1109/ICPADS.2015.89

    doi

  187. [2016]

    In Proceedings of the Twenty-Fifth International Joint Conference on Artificial In- telligence(New York, NY, USA)(IJCAI ’16), Subbarao Kambhampati (Ed.)

    KOGNAC: Efficient Encoding of Large Knowledge Graphs. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial In- telligence(New York, NY, USA)(IJCAI ’16), Subbarao Kambhampati (Ed.). International Joint Conferences on Artificial Intelligence Organizati...

  188. [2019]

    https://doi.org/10

    Hypergraph Neural Networks.Proceedings of the AAAI Conference on Artificial intelligence33, 01 (July 2019), 3558–3565. https://doi.org/10. 1609/aaai.v33i01.33013558

    2019

  189. [2020]

    2020), 19 pages

    Random Walks on Hypergraphs.Physical Review E101, 2, Article 022308 (Feb. 2020), 19 pages. https://doi.org/10.1103/PhysRevE.101. 022308

    2020 doi

  190. [2021]

    InProceedings of the 33rd Symposium on Parallelism in Algorithms and Architectures(Virtual Event)(SP AA ’21)

    Parallel Algorithms for Finding Large Cliques in Sparse Graphs. InProceedings of the 33rd Symposium on Parallelism in Algorithms and Architectures(Virtual Event)(SP AA ’21). Association for Computing Machinery, New York, NY, USA, 243–253. https://doi.org/10.1145/ 3409964.3461800

  191. [2023]

    Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries.ACM Comput. Surv. 56, 2, Article 31 (Sept. 2023), 40 pages. https://doi.org/10.1145/3604932

    2023 doi

  192. [2640]

    https://proceedings.neurips.cc/paper_files/paper/2021/hash/ 157792e4abb490f99dbd738483e0d2d4-Abstract.html

    2021

  193. [2743]

    https://doi.org/10.1109/TKDE.2017.2754499

    2017

  194. [3289]

    https://proceedings.neurips.cc/paper_files/paper/2020/hash/ 217eedd1ba8c592db97d0dbe54c7adfc-Abstract.html

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.