Paper Citation Record · LEDGER

A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2302.11640.

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

pith.paper-citation-record.v1
2302.11640 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:13:55.093941Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:19:57.666338Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation aadb03a3-272a-41b0-a672-6e87cb6dd45d · inbound

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection cites this paper.

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T22:13:55.093941Z

Source-reported events for the cited work

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Observation eba2e920-d41d-4ca6-994f-ef5577129edc · inbound

SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks cites this paper.

SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T19:05:45.906598Z

Source-reported events for the cited work

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Observation f60e69b2-858f-488a-8c58-b1bd70179586 · inbound

How to Use Graph Data in the Wild to Help Graph Anomaly Detection? cites this paper.

How to Use Graph Data in the Wild to Help Graph Anomaly Detection? A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:09.421093Z

Source-reported events for the cited work

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Observation 7fdea63b-89cf-46bd-8b39-37a9a1342f01 · inbound

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning cites this paper.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.483989Z

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Observation fbe7ebf0-0768-4961-bd18-0ea0d6aed06b · inbound

S2FGL: Spatial Spectral Federated Graph Learning cites this paper.

S2FGL: Spatial Spectral Federated Graph Learning A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:24.218949Z

Source-reported events for the cited work

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Observation c5bdbbf6-5439-444b-89e1-6afb1cb54d65 · inbound

Molecular Machine Learning Using Euler Characteristic Transforms cites this paper.

Molecular Machine Learning Using Euler Characteristic Transforms A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:12:50.244752Z

Source-reported events for the cited work

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Observation 9218ffc0-3bb5-4262-8d5f-4957443ea041 · inbound

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses cites this paper.

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 238

Resolution: unresolved; no resolver link, observed 2026-08-05T15:39:56.456526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79625630-3a2f-415b-b8a7-20437397d768 · inbound

Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs cites this paper.

Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T13:19:10.866489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc29342c-3b12-4c82-91be-e532ce93052e · inbound

L2G-Net: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations cites this paper.

L2G-Net: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 1994

Resolution: unresolved; no resolver link, observed 2026-08-02T21:56:55.052597Z

Source-reported events for the cited work

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Observation b13a5ce6-123a-4b5b-8047-73e64fe285aa · inbound

Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models cites this paper.

Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:16:01.483398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e168ec17-cfe0-4b0e-b46b-72fba0833450 · inbound

Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach cites this paper.

Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:41:26.290812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c542e64-a729-461b-b92c-8971fd8ec664 · inbound

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation cites this paper.

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:57:22.393294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 66c7c6ff-4591-4136-bbe9-85779226f4b8 · inbound

Random-Set Graph Neural Networks cites this paper.

Random-Set Graph Neural Networks A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:17:18.180941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ddd305ce-14ea-450a-830b-f555331a2338 · inbound

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:29:23.832518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d56fb45-a0a0-4c35-b760-060349be957b · inbound

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:55:05.714338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6149c8e2-cda6-4bb0-8f04-c399dc290c1b · inbound

Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification cites this paper.

Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 34

Resolution: malformed identifier; arxiv_id, observed 2026-05-21T08:34:05.381999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9a5312b-0abc-4949-b4b8-c1462827c988 · inbound

NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity cites this paper.

NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:44:00.824416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 653a9fc8-0766-4519-a909-012397b90b64 · inbound

Graph Navier Stokes Networks cites this paper.

Graph Navier Stokes Networks A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:56.847581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57d12672-b82e-4d30-a935-87ac3c537687 · inbound

Gaussian Sheaf Neural Networks cites this paper.

Gaussian Sheaf Neural Networks A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:49:40.971549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation edcae2c8-4f2e-4a0e-abd4-364a6c1188a0 · inbound

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach cites this paper.

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:01.552787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f5fce34c-84da-4b79-8677-1d1930fe7a41 · inbound

Generalist Graph Anomaly Detection via Prototype-Based Distillation cites this paper.

Generalist Graph Anomaly Detection via Prototype-Based Distillation A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T19:03:51.262320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58ec8565-6806-4620-95f8-3e84730a9b19 · inbound

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling cites this paper.

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:52:44.836736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d13aedb5-b128-4b85-b553-0682cdbf37ff · inbound

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs cites this paper.

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:16:16.201521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 08ebbf56-7b6c-4b2d-8579-46c4d48db3e5 · inbound

Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems cites this paper.

Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:19:57.667809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f720221c-ba08-4b36-a425-a9c4977f2f92 · inbound

HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks cites this paper.

HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T02:57:52.530442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.