Paper Citation Record · LEDGER

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation

As of 22 July 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2305.09958.

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

pith.paper-citation-record.v1
2305.09958 v5

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T08:44:23.340180Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-21T06:31:05.380196+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T06:10:26.634933Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T09:45:40.658923Z

Reference resolution

53 of 53 outbound references displayed

verified exact8
verified fuzzy45
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1fda8f56-0037-4691-b693-e07e171c61c4 · outbound

This paper cites Semi-supervised classifica- tion with graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Semi-supervised classifica- tion with graph convolutional networks

Reference 1

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Observation 2e5c47e6-7f09-48bf-a78d-2e9aec3e14bf · outbound

This paper cites Inductive representation learning on large graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Inductive representation learning on large graphs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.137908Z

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Observation cbf8e8ae-c954-4f5f-a65f-72d0577c2556 · outbound

This paper cites A Survey on Machine Learning Solutions for Graph Pattern Extraction.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation A Survey on Machine Learning Solutions for Graph Pattern Extraction

Reference 3

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Observation 78a4fd89-bdb9-408c-a5c0-1714b9929ac7 · outbound

This paper cites Spiking Graph Convolutional Networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Spiking Graph Convolutional Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:46:05.576777Z

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Observation 86efec72-cc49-4dac-b08d-0a0a35a7cf4f · outbound

This paper cites Fusing global domain information and local semantic information to classify financial documents.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Fusing global domain information and local semantic information to classify financial documents

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.133514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation c4c7159d-dde3-4395-8839-3b53a762bf14 · outbound

This paper cites Approximate graph propagation.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Approximate graph propagation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.130934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 9008b097-b683-45a4-9d6a-da603c6d9faf · outbound

This paper cites Scalable graph neural networks via bidirectional propagation.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Scalable graph neural networks via bidirectional propagation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.127709Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation fbf45bd4-6124-4a59-8fc3-e4200ca355d6 · outbound

This paper cites Graph Attention Networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Graph Attention Networks

Reference 8

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Observation 2bcc8963-5fe5-4f5e-8387-2a0758cd5405 · outbound

This paper cites Predict then propagate: Graph neural networks meet personal- ized pagerank.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Predict then propagate: Graph neural networks meet personal- ized pagerank

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.124805Z

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Observation 0644eefa-eb65-47de-9b16-8afe27ae3d2f · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.121375Z

Source-reported events for the cited work

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Observation aa2bb647-699f-41b2-aa49-5ed486194bb1 · outbound

This paper cites Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.118488Z

Source-reported events for the cited work

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Observation 3128da36-7694-4d41-8dab-7c4271fa8595 · outbound

This paper cites Geom-gcn: Geometric graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Geom-gcn: Geometric graph convolutional networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.004580Z

Source-reported events for the cited work

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Observation 6776ae60-a44e-4174-9c60-7fa42c6974b9 · outbound

This paper cites Graph pointer neural networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Graph pointer neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.115288Z

Source-reported events for the cited work

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Observation 72dcd47b-6a69-43d1-9f8d-5589927e47cb · outbound

This paper cites Non-local graph neural networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Non-local graph neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.112729Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5be7506e-15e2-4b6b-9268-5e7bf8c33ae8 · outbound

This paper cites Universal graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Universal graph convolutional networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.109780Z

Source-reported events for the cited work

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Observation e2d1797c-0d94-4578-a664-0ef5e7595686 · outbound

This paper cites Finding global homophily in graph neural networks when meeting heterophily.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Finding global homophily in graph neural networks when meeting heterophily

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.144486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4f0c1710-624b-4a4a-8958-7e33a49dac76 · outbound

This paper cites Large scale learning on non- homophilous graphs: New benchmarks and strong simple methods.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Large scale learning on non- homophilous graphs: New benchmarks and strong simple methods

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.106333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 040202a7-d89d-453e-964e-84d941651e86 · outbound

This paper cites Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.102797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 69ec7ecc-dbfc-4716-af08-766758e7df08 · outbound

This paper cites Simrank: a measure of structural- context similarity.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Simrank: a measure of structural- context similarity

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.097949Z

Source-reported events for the cited work

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Observation 475776d3-b5a5-4c8b-9abc-512ef5f3ea7e · outbound

This paper cites Cast: a correlation-based adaptive spectral clustering algorithm on multi-scale data.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Cast: a correlation-based adaptive spectral clustering algorithm on multi-scale data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.094141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation a765188a-2ced-40dd-bc56-2ed2fabae5d1 · outbound

This paper cites Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks

Reference 21

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Observation 7b9f4f64-87f1-4eab-83da-eb9a7b41fdaf · outbound

This paper cites Graph Neural Networks Beyond Compromise Between Attribute and Topology.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Graph Neural Networks Beyond Compromise Between Attribute and Topology

Reference 22

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4ed27430-c32f-4a82-a4da-9e14de4f0baa · outbound

This paper cites Exact single-source simrank computation on large graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Exact single-source simrank computation on large graphs

Reference 23

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 28d8605b-bd21-4ffa-960e-e31a7201b5b9 · outbound

This paper cites Representation learning on graphs with jumping knowledge networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Representation learning on graphs with jumping knowledge networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.085037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation d3887145-a93c-4213-8161-b3c818f1c765 · outbound

This paper cites Efficient top-k simrank-based simi- larity join.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Efficient top-k simrank-based simi- larity join

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.081762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation c4a96a89-b119-4e33-b8d6-4e348fecbf5d · outbound

This paper cites Efficient simrank track- ing in dynamic graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Efficient simrank track- ing in dynamic graphs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.078695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 8af38ed1-6694-4c7e-bb3d-22dd54eeaf49 · outbound

This paper cites GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:46:05.590987Z

Source-reported events for the cited work

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Observation 921a143c-2717-41ef-a74e-d9ae1ec0963a · outbound

This paper cites Massively Parallel Single-Source SimRanks in $o(\log n)$ Rounds.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Massively Parallel Single-Source SimRanks in $o(\log n)$ Rounds

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:46:05.571391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f80d1b10-79a4-4d86-9fa1-1dfbc359bd5a · outbound

This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.075826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3e402296-9402-457b-b8a1-ad96ab16d335 · outbound

This paper cites Adaptive universal generalized pagerank graph neural network.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Adaptive universal generalized pagerank graph neural network

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.072870Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 51362c75-9eaf-403f-a012-c19340bab537 · outbound

This paper cites Available: https://openreview.net/forum? id=n6jl7fLxrP.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Available: https://openreview.net/forum? id=n6jl7fLxrP

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.069786Z

Source-reported events for the cited work

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Observation f9bd65e2-2686-4e08-8b89-748e3e4e2fd8 · outbound

This paper cites Dif- fusion Improves Graph Learning.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Dif- fusion Improves Graph Learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.066694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 831a43c9-33af-4e1a-aaaf-9197277141e8 · outbound

This paper cites Scaling graph neural networks with approximate pagerank.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Scaling graph neural networks with approximate pagerank

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.063484Z

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Observation 1ac9aa6a-1b71-4dce-8671-5972c76b8c2a · outbound

This paper cites Do transformers really perform badly for graph representation?.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Do transformers really perform badly for graph representation?

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.060808Z

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Observation 15ac2f20-e8aa-4c80-bb90-7336a45445ce · outbound

This paper cites Simple and deep graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Simple and deep graph convolutional networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.008735Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 0079e326-297d-4861-8181-beb665851298 · outbound

This paper cites Simplifying graph convolutional networks.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Simplifying graph convolutional networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.058099Z

Source-reported events for the cited work

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Observation 95f4396f-19e4-4575-a6b5-89e57ca80963 · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for mod- eling both homophily and heterophily.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Gbk-gnn: Gated bi-kernel graph neural networks for mod- eling both homophily and heterophily

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.054943Z

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Observation 0e6d40b5-84b4-4939-a2bb-872753350891 · outbound

This paper cites Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.051936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 07b40a9e-472c-4943-be85-e72e0b6802ae · outbound

This paper cites Similarity-navigated graph neural networks for node classification.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Similarity-navigated graph neural networks for node classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.048263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3551c6fb-ddae-4a09-bbc8-73a4fbbbd65a · outbound

This paper cites Predicting global label relationship matrix for graph neural networks under heterophily.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Predicting global label relationship matrix for graph neural networks under heterophily

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.045283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation b8653f1b-3434-4aa3-9d12-c6a40dc58e75 · outbound

This paper cites Scara: Scalable graph neural networks with feature-oriented optimization.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Scara: Scalable graph neural networks with feature-oriented optimization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.041299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 2c8ad3b2-1e6f-48c1-9582-54ee4d72d1a2 · outbound

This paper cites LD2: Scalable heterophilous graph neural network with decoupled em- bedding.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation LD2: Scalable heterophilous graph neural network with decoupled em- bedding

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.011654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 429987cd-1873-4a6c-a849-a20b54fcf960 · outbound

This paper cites Scalable decoupling graph neural networks with feature-oriented optimization.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Scalable decoupling graph neural networks with feature-oriented optimization

Reference 43

Resolution: verified exact; doi, observed 2026-05-24T08:46:05.497459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 6fa18203-3be0-4da1-81f6-03fe8b1d3fe0 · outbound

This paper cites Bird: Efficient approximation of bidirectional hidden personalized pagerank.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Bird: Efficient approximation of bidirectional hidden personalized pagerank

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.038704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ada2281c-3f08-4885-aabd-9ceb9bd2993e · outbound

This paper cites Agenda: Robust personalized pager- anks in evolving graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Agenda: Robust personalized pager- anks in evolving graphs

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.035304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 41b971c1-7db5-4263-8677-23ed5d779736 · outbound

This paper cites Topology- monitorable contrastive learning on dynamic graphs.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Topology- monitorable contrastive learning on dynamic graphs

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.032515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4f4ab57c-a739-4046-adfb-216a0cb03f98 · outbound

This paper cites Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:46:05.567538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7a874d70-85ee-451a-b6ef-bf69c31b4b68 · outbound

This paper cites Multi- scale attributed node embedding.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Multi- scale attributed node embedding

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:46:06.029362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ba6c690d-0c18-41ed-b93b-3edfac5061cb · outbound

This paper cites Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking

Reference 49

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This paper cites Collective classification in network data.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Collective classification in network data

Reference 50

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This paper cites Query-driven active surveying for collective classification.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Query-driven active surveying for collective classification

Reference 51

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Observation cdc21e7b-37a0-4b87-854b-959960e806e7 · outbound

This paper cites On the use of arxiv as a dataset.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation On the use of arxiv as a dataset

Reference 52

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This paper cites Snap datasets: Stanford large network dataset collection.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Snap datasets: Stanford large network dataset collection

Reference 53

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Pith citing papers

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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning cites this paper.

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation

Reference 123

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