Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:11:53.317334Z
Paper Citation Record · LEDGER
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures
As of 22 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2507.00949.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
73 of 73 outbound references displayed
- verified exact4
- verified fuzzy47
- unresolved12
- parse uncertain0
- malformed identifier3
- metadata mismatch7
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c629fa31-6928-4e34-aec6-4bd37da3fa5c · outbound
This paper cites Lead–lag detection and network clustering for multivariate time series with an application to the us equity market,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Lead–lag detection and network clustering for multivariate time series with an application to the us equity market,
Reference 1
- Resolution
- malformed identifier
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b634f7fc-5b22-4fbd-bf55-bda148dc4c48 · outbound
This paper cites Scaling graph neural networks with approximate pagerank,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scaling graph neural networks with approximate pagerank,
Reference 2
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 913f9584-78b3-42db-ac8e-e4cc3339540e · outbound
This paper cites Addressing challenges of identifying geometrically diverse sets of crystalline porous materials,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Addressing challenges of identifying geometrically diverse sets of crystalline porous materials,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.343811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 66a1b7b4-05fd-487d-a36d-90e8c3c3308a · outbound
This paper cites Extracting insights from the shape of complex data using topology,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Extracting insights from the shape of complex data using topology,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.328285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7ec2a32c-e2b5-4f43-b6dd-ad5fc53308ce · outbound
This paper cites Graph evolution: Den- sification and shrinking diameters,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Graph evolution: Den- sification and shrinking diameters,
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cf4bd3bc-1053-4f6d-a90c-7a431453851d · outbound
This paper cites Collective dynamics of “small-world.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Collective dynamics of “small-world
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.296673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a2ff2fd9-120c-4b3e-b0f5-a593a8ba8f1b · outbound
This paper cites Emergence of scaling in random net- works,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Emergence of scaling in random net- works,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.281128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 180b441a-2194-40b3-9d6a-bd0a3787b198 · outbound
This paper cites Power-law distributions in empirical data,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Power-law distributions in empirical data,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.264570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 279ae343-08e2-4d87-a732-035e570fffdf · outbound
This paper cites Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.247724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8aae645d-9dc7-493f-bfef-13f1e611b084 · outbound
This paper cites Theoretical bounds on the network community profile from low-rank semi-definite programming,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Theoretical bounds on the network community profile from low-rank semi-definite programming,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.231708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e8c29add-4a1f-46d7-a6f7-ff221f015747 · outbound
This paper cites Shentu: Processing multi-trillion edge graphs on millions of cores in seconds,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Shentu: Processing multi-trillion edge graphs on millions of cores in seconds,
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:48.615465Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac2fcc1d-6ffe-4a3f-b75d-caca654115e0 · outbound
This paper cites Introducing the graph 500,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Introducing the graph 500,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.216147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e9fd92b6-7613-494f-b2fd-4c877b316d32 · outbound
This paper cites Pregel: A system for large-scale graph processing,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Pregel: A system for large-scale graph processing,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.200908Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 864d4db9-a83f-4c05-b42d-6a69b0e8c91e · outbound
This paper cites Powergraph: Distributed graph-parallel computation on natural graphs,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Powergraph: Distributed graph-parallel computation on natural graphs,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.185753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2f0d2a4d-3206-44e7-ac8d-c87a118ffda6 · outbound
This paper cites One trillion edges: graph processing at facebook-scale,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures One trillion edges: graph processing at facebook-scale,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:48.929165Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9319b323-662f-4012-b03d-730e363a792f · outbound
This paper cites Ligra: a lightweight graph processing framework for shared memory,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Ligra: a lightweight graph processing framework for shared memory,
Reference 16
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:56.413583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0683480b-41a0-4165-873d-4042df20d441 · outbound
This paper cites A lightweight infrastructure for graph analytics,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A lightweight infrastructure for graph analytics,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.170827Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 71ca0315-50b6-4d47-a5f1-74e7150b0377 · outbound
This paper cites Scalability! but at what cost?.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scalability! but at what cost?
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.155719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6c4f9143-dec4-4b08-9cc1-46c6d87ee4d2 · outbound
This paper cites Theoretically efficient parallel graph algorithms can be fast and scalable,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Theoretically efficient parallel graph algorithms can be fast and scalable,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.126119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb111371-00d9-4e86-ac44-aecd7de25531 · outbound
This paper cites Performance of the supercomputer fugaku for breadth-first search in graph500 benchmark,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Performance of the supercomputer fugaku for breadth-first search in graph500 benchmark,
Reference 20
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T21:11:59.111218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 417680fa-ed93-4946-a657-784661379f44 · outbound
This paper cites Tianhegraph: Customizing graph search for graph500 on tianhe supercomputer,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Tianhegraph: Customizing graph search for graph500 on tianhe supercomputer,
Reference 21
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:56.145310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1d5614e3-6abe-4683-9bed-face09d475fc · outbound
This paper cites Hpcg benchmark technical specification,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Hpcg benchmark technical specification,
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:49.633097Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a59307e1-7d5c-4de9-bb05-5de1e8ec27d8 · outbound
This paper cites (2022) Agile: Advanced graphic intelligence logical computing environment.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures (2022) Agile: Advanced graphic intelligence logical computing environment
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.096458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8bacf5c9-804b-422c-afe7-dbc5560bbc2a · outbound
This paper cites The PageRank citation ranking: Bringing order to the web,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures The PageRank citation ranking: Bringing order to the web,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.081819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0ef74413-738c-482b-92cb-a82f6825f93f · outbound
This paper cites Graph 500 results,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Graph 500 results,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.065769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6457b40d-5ec4-4008-a256-f7fc3f8e1347 · outbound
This paper cites Exploiting the block structure of the web for computing PageRank,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Exploiting the block structure of the web for computing PageRank,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.051001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2e1d92e2-e0a8-4466-9260-aa2949c72983 · outbound
This paper cites Extrapolation methods for accelerating PageRank computations,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Extrapolation methods for accelerating PageRank computations,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.036723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0d1e623a-78af-4fd3-8b12-3e3562f4fbd5 · outbound
This paper cites Scaling personalized web search,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scaling personalized web search,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.020749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4f5d1b66-3091-44a1-bb88-02205c188163 · outbound
This paper cites Efficient pagerank approximation via graph aggregation,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Efficient pagerank approximation via graph aggregation,
Reference 29
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:55.799139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 13ed6b21-61da-4292-81f6-b96da3df2d9a · outbound
This paper cites Fast parallel PageRank: A linear system approach,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Fast parallel PageRank: A linear system approach,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.006093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7edbfbc3-8449-4b61-a56d-5807f3eb9142 · outbound
This paper cites Scalable computing with power-law graphs: Experience with parallel PageRank,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scalable computing with power-law graphs: Experience with parallel PageRank,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.991049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 36476bd5-e957-4fd1-b2c0-00b47c3c3ad7 · outbound
This paper cites Bookmark-coloring algorithm for personalized PageRank computing,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Bookmark-coloring algorithm for personalized PageRank computing,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.975400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2b1a5099-b064-460d-acf3-0a1bb29c1237 · outbound
This paper cites A uniform approach to accelerated PageRank computa- tion,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A uniform approach to accelerated PageRank computa- tion,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.959430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a960192d-c8fb-41c9-9dcf-a06948dc3137 · outbound
This paper cites Scalable data-driven pagerank: Algorithms, system issues, and lessons learned,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scalable data-driven pagerank: Algorithms, system issues, and lessons learned,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.943574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 04150ddf-1cd9-4991-8939-840ecfc5e17f · outbound
This paper cites A sharp pagerank algorithm with applications to edge ranking and graph sparsification,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A sharp pagerank algorithm with applications to edge ranking and graph sparsification,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.928361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d032c410-3b7a-4431-a22a-8ab660ad4596 · outbound
This paper cites Fast incremental and personalized PageRank,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Fast incremental and personalized PageRank,
Reference 36
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:55.558168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5d43a523-f9e0-49b0-beac-d69cf47d6a13 · outbound
This paper cites A two-stage algorithm for computing PageRank and multistage generalizations,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A two-stage algorithm for computing PageRank and multistage generalizations,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.912637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9d12fd62-e19c-4358-bd3e-81f585ff7f9e · outbound
This paper cites A reordering for the pagerank problem,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A reordering for the pagerank problem,
Reference 38
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T21:11:58.896423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e7af16fb-8cd9-46d8-882a-6408bdf0d80d · outbound
This paper cites Scaling graph traversal to 281 trillion edges with 40 million cores,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Scaling graph traversal to 281 trillion edges with 40 million cores,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.880988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2c18782e-ed49-4f5c-9ce0-4a76d7718189 · outbound
This paper cites Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:51.162914Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95c0e026-230a-43fe-ac94-a5a782b81b21 · outbound
This paper cites Direction-optimizing breadth-first search,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Direction-optimizing breadth-first search,
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:51.228542Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 991366dd-acf2-4500-864e-aa916823b998 · outbound
This paper cites A parallel packed memory array to store dynamic graphs,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A parallel packed memory array to store dynamic graphs,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.865274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bade17ad-a2f5-4b06-a449-c8ed66f85d08 · outbound
This paper cites Batch-parallel compressed sparse row: A locality-optimized dynamic-graph representation,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Batch-parallel compressed sparse row: A locality-optimized dynamic-graph representation,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.847979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 831ebe0a-289e-474c-8e45-524901c74dde · outbound
This paper cites Byo: A unified framework for benchmarking large-scale graph containers,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Byo: A unified framework for benchmarking large-scale graph containers,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.832928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4cc7570d-90de-43e3-8d57-41c91e7f7173 · outbound
This paper cites Streaming sparse graphs using efficient dynamic sets,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Streaming sparse graphs using efficient dynamic sets,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.817047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 11756ec6-6137-4471-aa71-d685a8de735d · outbound
This paper cites Terrace: A hierarchical graph container for skewed dynamic graphs,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Terrace: A hierarchical graph container for skewed dynamic graphs,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.800115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b397cc60-edc7-4666-bb8d-be3f88e57d3b · outbound
This paper cites Advanced graphic intelligence logic computing environment,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Advanced graphic intelligence logic computing environment,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.782234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5f0b5032-a98a-4152-93db-33fddf9930bd · outbound
This paper cites Updown: A supercomputer co-designed for scalable graph processing,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Updown: A supercomputer co-designed for scalable graph processing,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.765705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cdbef342-c309-47e7-9342-ea62173ce5f0 · outbound
This paper cites UpDown: Programmable fine-grained Events for Scalable Performance on Irregular Applications.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures UpDown: Programmable fine-grained Events for Scalable Performance on Irregular Applications
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:11:55.225945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1c19b349-36cc-46f4-b30b-e3a945cd162a · outbound
This paper cites Efficiently exploiting irregular parallelism using keys at scale,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Efficiently exploiting irregular parallelism using keys at scale,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.750055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7362d976-c032-47e0-8962-a05e52106207 · outbound
This paper cites Updown: A novel architecture for unlimited memory parallelism,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Updown: A novel architecture for unlimited memory parallelism,
Reference 51
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:55.089471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ecbc25b6-4a28-43f5-88f0-875e07c1d9e6 · outbound
This paper cites Updown: Combining scalable address translation with locality control,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Updown: Combining scalable address translation with locality control,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.734400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8e784130-35e5-46ed-a4a9-c0c1d68185dc · outbound
This paper cites PolarFly: A Cost-Effective and Flexible Low-Diameter Topology.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures PolarFly: A Cost-Effective and Flexible Low-Diameter Topology
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:11:54.761687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c2dca0a8-f053-45b5-861f-de929f96cfbf · outbound
This paper cites PolarStar: Expanding the Scalability Horizon of Diameter-3 Networks.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures PolarStar: Expanding the Scalability Horizon of Diameter-3 Networks
Reference 54
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:11:54.553787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 371ee6c9-c087-4f5a-9b7e-0df2825f0ff9 · outbound
This paper cites The aurora exascale supercomputer,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures The aurora exascale supercomputer,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.718631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1810bfd5-b60b-4a31-bebc-43c212b32a12 · outbound
This paper cites SNAP Datasets: Stanford large network dataset collection,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures SNAP Datasets: Stanford large network dataset collection,
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:52.089775Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ba6921a5-4d24-4d66-b86c-b6b37d0d77d2 · outbound
This paper cites The gem5 simulator,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures The gem5 simulator,
Reference 57
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:54.378443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8cf75a18-f7ff-4dda-a6fd-292f522f440b · outbound
This paper cites A detailed and flexible cycle-accurate network-on-chip simulator,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A detailed and flexible cycle-accurate network-on-chip simulator,
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:52.189515Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 27da3d10-0524-40e5-b568-935f74fc4b68 · outbound
This paper cites The structural simulation toolkit,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures The structural simulation toolkit,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.513885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8f7fc286-e834-41cd-a5b4-61280875161b · outbound
This paper cites Merlin element library deep dive.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Merlin element library deep dive
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.397258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c5330b3e-d2a2-46d3-892f-84326be47f3b · outbound
This paper cites Polarstar: Expanding the horizon of diameter-3 networks,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Polarstar: Expanding the horizon of diameter-3 networks,
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:52.438490Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ebde6b3a-4b5c-4835-83d2-015355b077e1 · outbound
This paper cites An ai compute asic with optical attach to enable next generation scale-up architectures,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures An ai compute asic with optical attach to enable next generation scale-up architectures,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:58.070016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d299785d-e630-4d95-b4f6-c6bce2cbd529 · outbound
This paper cites Highly scalable large-scale asynchronous graph processing using actors,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Highly scalable large-scale asynchronous graph processing using actors,
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:52.624989Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 35840e6f-bfa6-4b8b-8ec7-0e2d152b2dc6 · outbound
This paper cites Pegasus: A peta-scale graph mining system implementation and observations,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Pegasus: A peta-scale graph mining system implementation and observations,
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:57.791967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6918025b-ad57-4a3e-ac96-c407af9caf8f · outbound
This paper cites Fast personalized PageRank on mapreduce,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Fast personalized PageRank on mapreduce,
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:57.561391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation fb77aea9-349a-4fba-9983-cd27cd223a13 · outbound
This paper cites Asymp: Fault- tolerant mining of massive graphs,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Asymp: Fault- tolerant mining of massive graphs,
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:57.282644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 76eea57d-262a-418d-8059-47720017bcc4 · outbound
This paper cites Rapids cugraph : multi-gpu pager- ank,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Rapids cugraph : multi-gpu pager- ank,
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:57.034322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3990187b-7583-4df1-996d-2aafb3c83e33 · outbound
This paper cites A distributed multi-gpu system for fast graph processing,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures A distributed multi-gpu system for fast graph processing,
Reference 68
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T21:11:53.938414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0ed27f88-bfc8-474c-baeb-45caecc3b1b2 · outbound
This paper cites Graphpeg: Accelerating graph processing on gpus,.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Graphpeg: Accelerating graph processing on gpus,
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:56.845321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 74e213c3-55a8-4a8a-ab37-9f0140481944 · outbound
This paper cites Efficient GPU Implementation of Static and Incrementally Expanding DF-P PageRank for Dynamic Graphs.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Efficient GPU Implementation of Static and Incrementally Expanding DF-P PageRank for Dynamic Graphs
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:11:53.585324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 858c0f3f-ccc9-4821-8623-c04735825b21 · outbound
This paper cites Available: http://blogs.usenix.org/conference/hotos15/ workshop-program/presentation/mcsherry.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Available: http://blogs.usenix.org/conference/hotos15/ workshop-program/presentation/mcsherry
Reference 2015
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:11:59.141268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f7458602-9cd8-488b-8ce1-546bc4a35ba9 · outbound
This paper cites Available: http://dx.doi.org/10.1145/3450440.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Available: http://dx.doi.org/10.1145/3450440
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:53.220481Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d0baa1d-f555-412d-8066-1f10bcbe9e67 · outbound
This paper cites Available: http://dx.doi.org/10.1145/3503221.3508403.
How Fast Can Graph Computations Go on Fine-grained Parallel Architectures Available: http://dx.doi.org/10.1145/3503221.3508403
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:11:51.107750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.