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Paper Citation Record · LEDGER

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

As of 19 August 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 2026-08-18T06:34:40.430872+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
  • metadata mismatch0

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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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arxiv_id, observed 2026-05-24T08:46:05.586310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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arxiv_id, observed 2026-05-24T08:46:05.576777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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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local_arxiv, observed 2026-05-24T08:46:05.581982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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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

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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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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 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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arxiv_id, observed 2026-05-24T08:46:05.590987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:a631b234f2a652ffe6cb6f0b268758328936116d8a9811b9d39789e1e886f06f

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:37745ff4d5c72749b4ec905f0cae87db316e533eb434d6c0e294d52f9b6fe476

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

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raw_fallback, observed 2026-05-24T08:46:06.072870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:0c8a22debc4a01ebd05308320d1e93078834b21b04f024856ee6077e87585383

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:9f205332dbbafc8d6797b16506cc08ad699c5604c1daf189edf6644baabbcd36

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

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raw_fallback, observed 2026-05-24T08:46:06.060808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:8053c72dd9fa824fe315cc41bc2ecbce7145b89941dadfdab42e5539281a004c

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

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:11b6559732bf35e3c2413fdae1023bb2451e4c6a8f970dfa76a76ebf51292552

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:b4f8818f09a7c703bda30cb6f7397768a561c96ef402ab244d15f96ae22bce41

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:e66ad81668d0e07bd537a01ab69afa58e6a1358ed92808c8f28101c0d457d56e

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:2ec6aff59498ce0c29427826943ea5e5dd35e64fcc4fd787d123e8c07a8cbbfa

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:22764db6484e04c6365f987be59a847a2dc28625aba64276987a976f0f9ae8da

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:d8cc015670fdaac2a38106975c66b984d65f05a4e74719d7ed92b7f59d88b1cb

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:4843e0047015bd7949d9c2b36a4fa3e2997d3b43972a250304ae8b1574dcdc00

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:60e58c4adf207c6f774f6c4496d95b55f06dbef1bd20fe346e7675b549fae2ef

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:810926a8ab81050c9da9272aba0380de16b04d84ec493c12f3af433e82957cc4

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:4fbb72361c6a4611fff1f95fcdf345badc5aa0fe46628d47fb0f4ab08dea9c71

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:613f0e43d3e5f99a3823e3d73fa3502a3bc7550650a2c17161e648e7303eabdd

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:1d19bcec11adbe0cc2b9e69c41768296eae6ab4e991bbdfdcb2f484f7f267571

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:b0c37aab93ffb0999b1369ef78d9c855fdb3da4a3e9ae1bdae43eb5297eabee6

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

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T08:46:06.026899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:c17fe1951c47047e85f1d83920ac26363ef399144ba76f1ddddf168c0d7d0814

Observation 8f4acaec-2be3-46ae-b1c6-6e349633205d · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T08:46:06.015242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:33df67c6ba4b286a251aeb0674965ea1be70a7d0062fc34832d731f1e36310ce

Observation 93ec8bb5-286d-4105-ac5e-8040fd118755 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T08:46:06.023808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:cbc7b74df8b009a372e52b735b61fe7e715b12e61c4c20c714320d505b060a79

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

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T08:46:06.020790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:7cf40bc3cd1e0cde701eb38f57a681d9f99f02c9601058db370a541e8951923e

Observation ef58ca05-3e5f-4794-8b2f-0af59f92c2f2 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T08:46:06.017864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:a93856056e6a398166f5e452aee0e0191ca7b586c2e28f87bca8412e8070217e

Pith citing papers

Observation 0a61fcb1-a327-4ae6-9da5-4a8979ac8bd7 · inbound

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

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T09:45:40.660564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-07-01T06:10:26.634933Z digest=sha256:c5fb23e75fc01bf4202050247f1761d5daeb50f126a0caf726c822e4b46cb1b1