Paper Citation Record · LEDGER

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning

As of 11 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2502.02302.

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

pith.paper-citation-record.v1
2502.02302 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:40:37.409071Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact1
verified fuzzy40
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1cd5eef-71aa-4af2-b294-ff5f65b514ba · outbound

This paper cites Learning graph representations with global structural information,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Learning graph representations with global structural information,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.134910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ace485ff-aa6a-44c6-a184-35f0ccf076b2 · outbound

This paper cites Tabularnet: A neural network architecture for understanding semantic structures of tabular data,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Tabularnet: A neural network architecture for understanding semantic structures of tabular data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.120412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e874d235-864d-4abb-a066-b3598410fe0f · outbound

This paper cites Structured subspace embedding on attributed networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Structured subspace embedding on attributed networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.106318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5b8eea7-53dd-4442-b64b-784630e2d3fe · outbound

This paper cites Structural deep network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Structural deep network embedding,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.092059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8306809b-2d38-46d3-bad1-b956f41c79fc · outbound

This paper cites Deep neural networks for learning graph representations,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Deep neural networks for learning graph representations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.077243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8df0dc5d-ccb3-4505-8ff9-da786ed980bf · outbound

This paper cites Content to node: Self- translation network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Content to node: Self- translation network embedding,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.061375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fb709cc1-6181-4fc7-94d1-62c673cafdda · outbound

This paper cites Graph Attention Networks.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Graph Attention Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.219516Z

Source-reported events for the cited work

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Observation c601eaa9-b447-4595-9b01-f625fd30acd5 · outbound

This paper cites A Critical Review of Recurrent Neural Networks for Sequence Learning.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning A Critical Review of Recurrent Neural Networks for Sequence Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.224914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 773490f5-528d-4c64-b16f-c305a8b5a62a · outbound

This paper cites Heterogeneous graph neural networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Heterogeneous graph neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.044739Z

Source-reported events for the cited work

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Observation cb67eddb-12c4-41f5-a9be-bb9f73816199 · outbound

This paper cites Heterogeneous graph attention network,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Heterogeneous graph attention network,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.030137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9485c01a-04a7-41ba-b3c2-00d15718eefd · outbound

This paper cites Explicit message-passing heterogeneous graph neural network,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Explicit message-passing heterogeneous graph neural network,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.015921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 82303fec-2eff-4ad2-8a6e-cf01976e3a15 · outbound

This paper cites Diffmg: Differentiable meta graph search for heterogeneous graph neural networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Diffmg: Differentiable meta graph search for heterogeneous graph neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:38.000670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ac0bdb54-b275-4b0c-a9c3-de3230217c55 · outbound

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

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.248683Z

Source-reported events for the cited work

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Observation 6f04d8e2-a238-4550-81e6-fc16fd6c96f6 · outbound

This paper cites Multiplex heterogeneous graph convolutional network,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Multiplex heterogeneous graph convolutional network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.986024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 78cc9fa5-ce79-44fe-a34b-a71d77701251 · outbound

This paper cites word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.258411Z

Source-reported events for the cited work

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Observation cad66429-dc5e-4e1a-b953-2258efb85ac6 · outbound

This paper cites Nonlinear dimensionality reduction by locally linear embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Nonlinear dimensionality reduction by locally linear embedding,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.263311Z

Source-reported events for the cited work

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Observation f52ececd-5a65-43c3-ba23-2539640993cd · outbound

This paper cites A global geometric framework for nonlinear dimensionality reduction,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning A global geometric framework for nonlinear dimensionality reduction,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T12:40:37.268123Z

Source-reported events for the cited work

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Observation 05b80dd7-666d-4339-bec1-da78ad66787b · outbound

This paper cites Laplacian eigenmaps and spectral techniques for embedding and clustering,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Laplacian eigenmaps and spectral techniques for embedding and clustering,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.952521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 77619370-b93f-4f15-ae72-608627cc78aa · outbound

This paper cites Dis- tributed representations of words and phrases and their compositionality,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Dis- tributed representations of words and phrases and their compositionality,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.938484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cabdb0d1-0cc7-4abe-a9f4-d5a036f2783d · outbound

This paper cites Deepwalk: Online learning of social representations,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Deepwalk: Online learning of social representations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.924608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 23271826-f3a2-47e4-bf6e-bfcab29c9271 · outbound

This paper cites node2vec: Scalable feature learning for networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning node2vec: Scalable feature learning for networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.910530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7f689b13-4422-46f1-a4d6-c19681780d10 · outbound

This paper cites Community preserving network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Community preserving network embedding,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.896357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1422a455-9082-41c4-9a4f-cd33dea30fbc · outbound

This paper cites Attributed network embedding with micro-meso structure,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Attributed network embedding with micro-meso structure,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.882322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7d3093b-c6b6-4eef-8082-a6a8c69823f2 · outbound

This paper cites Extracting semantic representations from word co-occurrence statistics: A computational study,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Extracting semantic representations from word co-occurrence statistics: A computational study,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.868052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 10d7e1aa-f951-4eb9-a125-00c99e0995a5 · outbound

This paper cites Cane: Context-aware network embedding for relation modeling,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Cane: Context-aware network embedding for relation modeling,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.853630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 07509191-0281-4a18-b118-287b8b71e295 · outbound

This paper cites Content to node: Self-translation network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Content to node: Self-translation network embedding,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.839167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 29033539-f5a1-49b2-85a1-d7fc69cc8f7b · outbound

This paper cites Deep attributed network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Deep attributed network embedding,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.824718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e7bea3d-48fe-4fda-8e2a-ed421e7af0d8 · outbound

This paper cites Self-paced network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Self-paced network embedding,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.810021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a3f70c59-becf-4923-b666-f419774a4dc1 · outbound

This paper cites Deep network embedding for graph represen- tation learning in signed networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Deep network embedding for graph represen- tation learning in signed networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.794818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e50dbfe5-b3ae-41f5-a95a-a766a62b9ba3 · outbound

This paper cites Node pair information preserving network embedding based on adversarial networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Node pair information preserving network embedding based on adversarial networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.779657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1679ae50-e655-41a2-92cf-40fabf23dc42 · outbound

This paper cites Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

Reference 31

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Source-reported events for the cited work

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Observation 4aa00e4a-c818-45cf-99cf-78078c00146c · outbound

This paper cites Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.765094Z

Source-reported events for the cited work

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Observation ba3af17f-3f05-4872-9409-548f5f62feb6 · outbound

This paper cites Dynamic hypergraph neural networks.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Dynamic hypergraph neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.750657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 109590f5-fcf3-4d87-a69e-813a28a68d8d · outbound

This paper cites Inductive representation learning on large graphs,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Inductive representation learning on large graphs,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.736281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7700a03a-79e5-4b08-a2da-0ad247c2a5f8 · outbound

This paper cites Learning graph embedding with adversarial training methods,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Learning graph embedding with adversarial training methods,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.722157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f9477aba-e681-4a1f-bb00-d00fec7b6496 · outbound

This paper cites Censnet: Convolution with edge-node switching in graph neural networks.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Censnet: Convolution with edge-node switching in graph neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.707273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6e587cbc-6080-41bb-949d-eacf86e3f5cc · outbound

This paper cites A vectorized relational graph convolutional network for multi-relational network alignment.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning A vectorized relational graph convolutional network for multi-relational network alignment

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.693082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 50d6db17-386d-4fb3-bc41-a61cda45b2ab · outbound

This paper cites RealFormer: Transformer Likes Residual Attention.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning RealFormer: Transformer Likes Residual Attention

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:40:37.452100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21c6b700-220d-4451-ad04-197ce1ec2d31 · outbound

This paper cites Freebase: a collaboratively created graph database for structuring human knowledge,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Freebase: a collaboratively created graph database for structuring human knowledge,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.678461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c01f377f-5ba8-4f40-87e5-ec688c59f897 · outbound

This paper cites Heterogeneous network representation learning: A unified framework with survey and benchmark,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Heterogeneous network representation learning: A unified framework with survey and benchmark,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.663359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2a776a54-c838-4516-870e-172eb0e0e521 · outbound

This paper cites Graph transformer networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Graph transformer networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.649219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d8b9847e-a89f-4487-8e56-d7bf7af7d41f · outbound

This paper cites Heterogeneous graph transformer,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Heterogeneous graph transformer,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.634544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 704f2c7f-7a8e-4712-93c6-77e78ff82ee7 · outbound

This paper cites Modeling relational data with graph convolutional networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Modeling relational data with graph convolutional networks,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.618895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe1e8eef-5484-4b5a-b648-73ccbc1fe307 · outbound

This paper cites Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.602923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17f617e3-434a-4859-98c1-88a97f6b5e0a · outbound

This paper cites Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.588310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 939fb67d-69c3-4bda-b825-cb3cd4cd6c17 · outbound

This paper cites Simple and efficient heterogeneous graph neural network,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Simple and efficient heterogeneous graph neural network,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.573261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87290539-4637-4013-8257-0169aa5e3020 · outbound

This paper cites Multi-hierarchical spatial- temporal graph convolutional networks for traffic flow forecasting,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Multi-hierarchical spatial- temporal graph convolutional networks for traffic flow forecasting,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.557926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1081a5f-3702-4fee-8a21-fe33cd646bb8 · outbound

This paper cites Attributed social network embedding,.

EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning Attributed social network embedding,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:37.541259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.