Pith. sign in

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
  • metadata mismatch0

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

Resolution
unresolved
no resolver link, observed 2026-08-11T17:15:25.552790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:15:25.552790Z digest=sha256:2549839f370a24c2ce18a5e47ca7de2639783e8e58978f23a88e0c08283a8114

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.159181Z

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.

source=pdf_text observed=2026-08-11T17:15:25.558973Z digest=sha256:7b928f8f610658a477cc5a978f95138abed65a311643ee750a176e7ade87b46d

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.141643Z

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.

source=pdf_text observed=2026-08-11T17:15:25.564291Z digest=sha256:f4f69e2f53107c00c9c835e88dd8943becb33ae566a10a82154267a4ce803a26

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.126387Z

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.

source=pdf_text observed=2026-08-11T17:15:25.569821Z digest=sha256:f4c9905bc5954722a4f93e3c2073179f5fcd764407c601edab882ed113f41384

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.111355Z

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.

source=pdf_text observed=2026-08-11T17:15:25.575414Z digest=sha256:e49f9a421443117883d0760879b86cecaaebcc6ccb1340600f8597966e11bf43

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.096095Z

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.

source=pdf_text observed=2026-08-11T17:15:25.580974Z digest=sha256:ccf570e5186cf30994ade7a34b3967cb5a61e801e080810bee1105d7584f535f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.080973Z

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.

source=pdf_text observed=2026-08-11T17:15:25.587840Z digest=sha256:6ecd61f69b567524326af61bdbfa8c01713426958e2c835ea784ec988c88053e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.065390Z

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.

source=pdf_text observed=2026-08-11T17:15:25.594040Z digest=sha256:556800878565dba789fa2dfebec375c89683e301b7e29380d3ac9273437921f4

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.050065Z

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.

source=pdf_text observed=2026-08-11T17:15:25.599412Z digest=sha256:46e1d4062a0924caee0acfc06ff1012056922bc5a0d22493f0c6307f6d3346b8

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

This paper cites an unresolved cited work.

Revisiting Graph Homophily Measures Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-11T17:15:26.033459Z

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.

source=pdf_text observed=2026-08-11T17:15:25.604781Z digest=sha256:99e9e7eb4f1b04a424c9933ebbb36aefc690dd7e36be3d16c404a95f21a24c6b

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

Resolution
unresolved
no resolver link, observed 2026-08-11T17:15:25.609744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:15:25.609744Z digest=sha256:b67aab49ed707ad81aeb87498864a43421ef9ed36a483240cf3cf80077e9bb2a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.016537Z

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.

source=pdf_text observed=2026-08-11T17:15:25.614961Z digest=sha256:2b0880bdc9f8819f36d73f2c7c2187b5c18809f0ba39a1c9560348ca25b3fb8e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:26.000007Z

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.

source=pdf_text observed=2026-08-11T17:15:25.620348Z digest=sha256:46ccb4b79d3bc23162af5b1d2296a8de1c4d3acfa2077931854315313f8879da

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.981611Z

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.

source=pdf_text observed=2026-08-11T17:15:25.625059Z digest=sha256:1cded2345e7ac12a4168f8765697304c271df4c658ff04aad960ca13f0d9a980

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.965469Z

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.

source=pdf_text observed=2026-08-11T17:15:25.629723Z digest=sha256:b0d5690b5d970590216311c7a8a191572697a3589b3edaf9a3fba39c731f3be8

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.949073Z

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.

source=pdf_text observed=2026-08-11T17:15:25.634439Z digest=sha256:87ed26267f6141a02bec689c250c9be15afeba0970de6cc75e8d203b6ebb1b3b

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

This paper cites Lee Giles, Kurt D.

Revisiting Graph Homophily Measures Lee Giles, Kurt D

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.931865Z

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.

source=pdf_text observed=2026-08-11T17:15:25.639590Z digest=sha256:f0e009764841c0a386995271be9c84f7ce2b1bb97ffc1272e4d64064b1f94990

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.915634Z

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.

source=pdf_text observed=2026-08-11T17:15:25.644492Z digest=sha256:f54d5aa1c6515a4e080ade924b852861b07e595d3d585cdf3a9a5d5869fb05bd

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

Resolution
unresolved
no resolver link, observed 2026-08-11T17:15:25.649572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:15:25.649572Z digest=sha256:2aa5dd01192d0b672462a1c055996086875bbe88aea1c87db7eef3a518e074a6

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.888612Z

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.

source=pdf_text observed=2026-08-11T17:15:25.654337Z digest=sha256:43d9efe7c6a8892b035b02a226456ec930cf56fbbc755538a8874cba40dc036f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.872754Z

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.

source=pdf_text observed=2026-08-11T17:15:25.660411Z digest=sha256:0549d55233b053a2bb1b24859f7d53186c73399931be9618cfca7b948372d1ee

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.855828Z

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.

source=pdf_text observed=2026-08-11T17:15:25.666243Z digest=sha256:a93e10e80963c901b2045898750fa85bd37099be6122e03d2a0b9db1b28d8115

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.838309Z

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.

source=pdf_text observed=2026-08-11T17:15:25.671514Z digest=sha256:d332f1f6024040e98167f42bb7ea03734777bf4134fb996586586cf645f7831d

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.821220Z

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.

source=pdf_text observed=2026-08-11T17:15:25.676242Z digest=sha256:a19ef1693c3d4bbb8b7d10ec3cdd361cc93c5ed85dd2751cde4f82371fbccffe

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.804889Z

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.

source=pdf_text observed=2026-08-11T17:15:25.681171Z digest=sha256:1e219e5f37d4504d1dfedcd955be2bf6a79245cdb244c6b939abda5e39dbaa28

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.788231Z

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.

source=pdf_text observed=2026-08-11T17:15:25.685838Z digest=sha256:b844bffed35747af822dbb8adbec7fcad5bbda72f126e01390d22d1a7b046af3

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:15:25.768945Z

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.

source=pdf_text observed=2026-08-11T17:15:25.690922Z digest=sha256:bff2d878bd271edb51a0a9c8081d929b3bea4c4f096f37d67d524609c7edfcc2

Pith citing papers

No inbound Pith citation observations are available.