Paper Citation Record · LEDGER

Enhancing the Utility of Higher-Order Information in Relational Learning

As of 21 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2502.09570.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.09570 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:04:19.491683Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact4
verified fuzzy10
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c95e085-e40f-423e-823f-fef788944890 · outbound

This paper cites an unresolved cited work.

Enhancing the Utility of Higher-Order Information in Relational Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:04:19.760003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f1af8a8-3abb-4b48-98c9-5fe56b969720 · outbound

This paper cites an unresolved cited work.

Enhancing the Utility of Higher-Order Information in Relational Learning Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:04:19.710909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 890ff1d0-3eef-4430-98ca-fb83b3db3fea · outbound

This paper cites an unresolved cited work.

Enhancing the Utility of Higher-Order Information in Relational Learning Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:04:19.690549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9c7d7721-eac2-48d3-b0c3-c7a17a1e55cb · outbound

This paper cites UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks.

Enhancing the Utility of Higher-Order Information in Relational Learning UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.418322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaa93eeb-0cd9-489b-903e-264b43428680 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Enhancing the Utility of Higher-Order Information in Relational Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.421493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7de4f69-1d08-4725-956f-63572923f411 · outbound

This paper cites Hypergraph transformer for semi-supervised classification.

Enhancing the Utility of Higher-Order Information in Relational Learning Hypergraph transformer for semi-supervised classification

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.795918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 96d82c44-a2c5-4079-ad39-14cb6982da7a · outbound

This paper cites On the Expressiveness and Generalization of Hypergraph Neural Networks.

Enhancing the Utility of Higher-Order Information in Relational Learning On the Expressiveness and Generalization of Hypergraph Neural Networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T21:04:19.579019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5699cb44-d076-41e2-ad72-1aee971d2cb4 · outbound

This paper cites an unresolved cited work.

Enhancing the Utility of Higher-Order Information in Relational Learning Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:04:19.680238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa808313-f534-4901-aac5-5503d9bd21a9 · outbound

This paper cites TopoBench: A Framework for Benchmarking Topological Deep Learning.

Enhancing the Utility of Higher-Order Information in Relational Learning TopoBench: A Framework for Benchmarking Topological Deep Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.440016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166e390d-7b10-4d0c-a0fa-905970ce60d6 · outbound

This paper cites From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness.

Enhancing the Utility of Higher-Order Information in Relational Learning From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.450862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11f2dc9b-021b-4bb4-9f0d-a8a33a7d118e · outbound

This paper cites 36 E.4 Additional Plots Figure 10: The pair 0 of the regular category in BREC.

Enhancing the Utility of Higher-Order Information in Relational Learning 36 E.4 Additional Plots Figure 10: The pair 0 of the regular category in BREC

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.670382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5dfbc92e-dbac-4abe-9438-135404a376f2 · outbound

This paper cites an unresolved cited work.

Enhancing the Utility of Higher-Order Information in Relational Learning Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:04:19.769178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1d95beb7-97c1-464b-9d7c-c9bb5c9810fc · outbound

This paper cites The probability of a random walk transitioning from node i to j is given by −Lij = Aij di.

Enhancing the Utility of Higher-Order Information in Relational Learning The probability of a random walk transitioning from node i to j is given by −Lij = Aij di

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.741323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f207c777-355d-4583-b75f-fe9303d9f290 · outbound

This paper cites B.2.2 UniGIN UniGIN also follows the two-phase scheme (see Eq.

Enhancing the Utility of Higher-Order Information in Relational Learning B.2.2 UniGIN UniGIN also follows the two-phase scheme (see Eq

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.731051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71053da8-11ef-4d1b-b17e-277510e4369a · outbound

This paper cites We use the same pre-processed hypergraphs as in Yadati et al.

Enhancing the Utility of Higher-Order Information in Relational Learning We use the same pre-processed hypergraphs as in Yadati et al

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.700616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65378c3e-ea7e-41b0-98e9-1c45c9a4ec6e · outbound

This paper cites How Powerful are Graph Neural Networks?.

Enhancing the Utility of Higher-Order Information in Relational Learning How Powerful are Graph Neural Networks?

Reference 1968

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.447195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 205732f6-2fd8-478e-81d4-59078510f3e7 · outbound

This paper cites typhimurium TA98.

Enhancing the Utility of Higher-Order Information in Relational Learning typhimurium TA98

Reference 1973

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.721322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5aab17d2-b986-49d0-822d-8dc3c16fd87e · outbound

This paper cites Graph Neural Networks with Learnable Structural and Positional Representations.

Enhancing the Utility of Higher-Order Information in Relational Learning Graph Neural Networks with Learnable Structural and Positional Representations

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.403666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b701a01d-e3da-48a2-94a0-97730238700a · outbound

This paper cites Here, we focus on clique expansion, which we empirically found to be the best performing expansion.

Enhancing the Utility of Higher-Order Information in Relational Learning Here, we focus on clique expansion, which we empirically found to be the best performing expansion

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.777820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cafc9aed-8b0e-4e14-94c0-a93948c7d24b · outbound

This paper cites Topological Deep Learning: Going Beyond Graph Data.

Enhancing the Utility of Higher-Order Information in Relational Learning Topological Deep Learning: Going Beyond Graph Data

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.414649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82fb33b6-4b4c-455d-b474-920625ca47e0 · outbound

This paper cites A new model for learning in graph do- mains.

Enhancing the Utility of Higher-Order Information in Relational Learning A new model for learning in graph do- mains

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.804379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 18d3f76b-9455-43eb-a211-65e970622ede · outbound

This paper cites Forman’s ricci curvature-from networks to hypernetworks.

Enhancing the Utility of Higher-Order Information in Relational Learning Forman’s ricci curvature-from networks to hypernetworks

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.787460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 08e7011a-3561-4be6-bada-c2afdafec7f4 · outbound

This paper cites An Empirical Study of Realized GNN Expressiveness.

Enhancing the Utility of Higher-Order Information in Relational Learning An Empirical Study of Realized GNN Expressiveness

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.443714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef62a23f-54d2-4842-aaba-61b92e587454 · outbound

This paper cites The Dirichlet energy E(f ) of a scalar function on a hypergraph is defined as E(f ) = 1 2 X e∈E X {u,v}⊆e 1 |e| f (u)p d(u) − f (v)p d(v) !2.

Enhancing the Utility of Higher-Order Information in Relational Learning The Dirichlet energy E(f ) of a scalar function on a hypergraph is defined as E(f ) = 1 2 X e∈E X {u,v}⊆e 1 |e| f (u)p d(u) − f (v)p d(v) !2

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:04:19.750801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6855e94a-50f6-4e7a-905c-e300d8417f12 · outbound

This paper cites Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework.

Enhancing the Utility of Higher-Order Information in Relational Learning Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.399795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f8e073e-f5e0-48b2-9208-b9d7a9433c13 · outbound

This paper cites Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data.

Enhancing the Utility of Higher-Order Information in Relational Learning Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T21:04:19.660057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ad7d776-db9c-4312-b908-e3848e2d208a · outbound

This paper cites A simple yet effective baseline for non-attributed graph classification.

Enhancing the Utility of Higher-Order Information in Relational Learning A simple yet effective baseline for non-attributed graph classification

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T21:04:19.395944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6045a8b6-d41a-46a5-b864-cb64a045a483 · outbound

This paper cites HyperNetX: A Python package for modeling complex network data as hypergraphs.

Enhancing the Utility of Higher-Order Information in Relational Learning HyperNetX: A Python package for modeling complex network data as hypergraphs

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T21:04:19.565265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b06f1287-8e75-4e46-9425-f599d9c7c4fa · outbound

This paper cites Chemically inspired Erd\H{o}s-R\'enyi oriented hypergraphs.

Enhancing the Utility of Higher-Order Information in Relational Learning Chemically inspired Erd\H{o}s-R\'enyi oriented hypergraphs

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-07T21:04:19.619073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.