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Paper Citation Record · LEDGER

Revisiting Graph Homophily Measures

As of 12 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2412.09663.

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

pith.paper-citation-record.v1
2412.09663 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:15:25.690922Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

27 of 27 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved4
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8bd5c10-1a9f-420d-a3f5-43ea374fe0ef · outbound

This paper cites The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges.

Revisiting Graph Homophily Measures The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0d7336f2-a5c0-49bd-8cbc-21116ed32997 · outbound

This paper cites Be- yond homophily in graph neural networks: Current limitations and effective designs.

Revisiting Graph Homophily Measures Be- yond homophily in graph neural networks: Current limitations and effective designs

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4c482dc4-52c6-4532-a109-2a4aaa836153 · outbound

This paper cites Is homophily a necessity for graph neural networks? In International Conference on Learning Representations, 2022.

Revisiting Graph Homophily Measures Is homophily a necessity for graph neural networks? In International Conference on Learning Representations, 2022

Reference 3

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Observation 943284cf-15e3-4d70-a189-5fd2db3337b6 · outbound

This paper cites Revisiting heterophily for graph neural networks.

Revisiting Graph Homophily Measures Revisiting heterophily for graph neural networks

Reference 4

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Source-reported events for the cited work

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Observation 267cc8a6-1f42-48d0-9343-01cfcc61edb0 · outbound

This paper cites A critical look at the evaluation of GNNs under heterophily: Are we re- ally making progress? 2023.

Revisiting Graph Homophily Measures A critical look at the evaluation of GNNs under heterophily: Are we re- ally making progress? 2023

Reference 5

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Source-reported events for the cited work

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Observation d43fd6da-c879-4a67-b705-f39176f77e70 · outbound

This paper cites Characteriz- ing graph datasets for node classification: Homophily-heterophily dichotomy and beyond.

Revisiting Graph Homophily Measures Characteriz- ing graph datasets for node classification: Homophily-heterophily dichotomy and beyond

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 41e231cc-dea4-43d5-b718-7d2d6ea56dc9 · outbound

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

Revisiting Graph Homophily Measures Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 9170c2f1-c8fb-432f-a5a7-f524c323c32d · outbound

This paper cites Geom-GCN: Geo- metric graph convolutional networks.

Revisiting Graph Homophily Measures Geom-GCN: Geo- metric graph convolutional networks

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d7f9f38e-981a-4a28-9c5f-9e86fc0cdf16 · outbound

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

Revisiting Graph Homophily Measures Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 228e3a89-444e-44d5-a58d-2bb7b693c83b · outbound

This paper cites an unresolved cited work.

Revisiting Graph Homophily Measures Unresolved cited work

Reference 10

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3e236507-a41b-4af1-979d-22c5f6f05667 · outbound

This paper cites What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks.

Revisiting Graph Homophily Measures What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks

Reference 11

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Source-reported events for the cited work

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Observation 687af56d-fae1-4404-88d7-6c4add82cc0a · outbound

This paper cites When do graph neural networks help with node classification? In- vestigating the homophily principle on node distinguishability.

Revisiting Graph Homophily Measures When do graph neural networks help with node classification? In- vestigating the homophily principle on node distinguishability

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation ffd30179-5902-4fc2-b935-3709ab68fb17 · outbound

This paper cites Graph database repository for graph based pattern recognition and machine learning.

Revisiting Graph Homophily Measures Graph database repository for graph based pattern recognition and machine learning

Reference 13

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 10ed2b20-b76d-41c7-b4e3-4ede081f3517 · outbound

This paper cites Good classification measures and how to find them.

Revisiting Graph Homophily Measures Good classification measures and how to find them

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 60bd8b94-3245-46a1-814a-476a250e8b82 · outbound

This paper cites Higher-order homophily on simplicial complexes.

Revisiting Graph Homophily Measures Higher-order homophily on simplicial complexes

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4e780484-663f-4db3-a062-08b797a6c28d · outbound

This paper cites On the inadequacy of nominal assortativity for assessing homophily in networks.

Revisiting Graph Homophily Measures On the inadequacy of nominal assortativity for assessing homophily in networks

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 420498bd-dff3-440e-bffe-b9903b698662 · outbound

This paper cites Lee Giles, Kurt D.

Revisiting Graph Homophily Measures Lee Giles, Kurt D

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8ae9e7b6-bfe3-4334-b2d7-be0571198dd3 · outbound

This paper cites Automating the construction of internet portals with machine learning.

Revisiting Graph Homophily Measures Automating the construction of internet portals with machine learning

Reference 18

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Source-reported events for the cited work

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Observation ed3eb9e4-82bb-414d-8d98-1d032ec6d35c · outbound

This paper cites Collective classification in network data.

Revisiting Graph Homophily Measures Collective classification in network data

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 03ea3ce7-8390-4352-8f2a-83e77d04f31c · outbound

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

Revisiting Graph Homophily Measures Query-driven active surveying for collective classification

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 340800f5-c8eb-4e39-993b-dc39b75c0661 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

Revisiting Graph Homophily Measures Revisiting semi-supervised learning with graph embeddings

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 24280517-b5c9-409d-a3f1-4550e376389e · outbound

This paper cites Pitfalls of graph neural network evaluation.

Revisiting Graph Homophily Measures Pitfalls of graph neural network evaluation

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8bc710eb-af72-473a-9242-cf1cdede42cb · outbound

This paper cites Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models.

Revisiting Graph Homophily Measures Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b6c73b45-75d9-4661-a8b7-c1c819ef85c2 · outbound

This paper cites Multi-scale attributed node embedding.

Revisiting Graph Homophily Measures Multi-scale attributed node embedding

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation a456f828-2276-4bcc-9b6a-cdf6f9d23aa9 · outbound

This paper cites Social influence analysis in large-scale networks.

Revisiting Graph Homophily Measures Social influence analysis in large-scale networks

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation dabd8905-43d4-4dae-8560-2a25a16cd51b · outbound

This paper cites GraphSAINT: Graph sampling based inductive learning method.

Revisiting Graph Homophily Measures GraphSAINT: Graph sampling based inductive learning method

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8243c506-5015-4a01-bece-ad59ca0551e4 · outbound

This paper cites 1 4 1 4 1 4 1 4 # , L 2 =.

Revisiting Graph Homophily Measures 1 4 1 4 1 4 1 4 # , L 2 =

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Pith citing papers

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