Paper Citation Record · LEDGER

THeGCN: Temporal Heterophilic Graph Convolutional Network

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 4 inbound Pith citation observations for arXiv:2412.16435.

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

pith.paper-citation-record.v1
2412.16435 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:38:15.980249Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:18:20.158523Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T17:31:22.298494Z

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy23
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ed25026-02c3-4e99-a1d7-91ee9404acbb · outbound

This paper cites Adaptive graph convolutional recurrent net- work for traffic forecasting.Advances in neural information processing systems, 33:17804–17815,.

THeGCN: Temporal Heterophilic Graph Convolutional Network Adaptive graph convolutional recurrent net- work for traffic forecasting.Advances in neural information processing systems, 33:17804–17815,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.404824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57e8a058-a1e2-4a4c-9903-09cc14e9c10b · outbound

This paper cites Do we really need complicated model architectures for temporal networks? In The Eleventh Inter- national Conference on Learning Representations,.

THeGCN: Temporal Heterophilic Graph Convolutional Network Do we really need complicated model architectures for temporal networks? In The Eleventh Inter- national Conference on Learning Representations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.373004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 541aeee7-7806-49ba-8a58-39bdb968b5e7 · outbound

This paper cites Gcn-se: Attention as explainability for node clas- sification in dynamic graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Gcn-se: Attention as explainability for node clas- sification in dynamic graphs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.350869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c21505ab-37d2-46a8-a877-9cf226bc375a · outbound

This paper cites Artificial neural networks (the multilayer percep- tron)—a review of applications in the atmospheric sciences.

THeGCN: Temporal Heterophilic Graph Convolutional Network Artificial neural networks (the multilayer percep- tron)—a review of applications in the atmospheric sciences

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.340171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6067e162-eb02-4479-abc2-efc5b29d99ec · outbound

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

THeGCN: Temporal Heterophilic Graph Convolutional Network Semi-Supervised Classification with Graph Convolutional Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.883711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c6aa91-4e8e-4158-8674-bf8c367fd9d7 · outbound

This paper cites Predicting dynamic embedding trajectory in tem- poral interaction networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Predicting dynamic embedding trajectory in tem- poral interaction networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.311655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5746161-a193-4ca1-b2ae-01b6d2d12644 · outbound

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

THeGCN: Temporal Heterophilic Graph Convolutional Network Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.301314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ba5e8c2-1935-417c-81c1-2d9c4e9d6493 · outbound

This paper cites Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?.

THeGCN: Temporal Heterophilic Graph Convolutional Network Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.901891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0454a08-6282-4bcf-aecb-8cbd00b8df44 · outbound

This paper cites Revisiting Heterophily For Graph Neural Networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Revisiting Heterophily For Graph Neural Networks

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:38:16.088710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9997d171-78c8-4662-8c9d-582fa17c24f4 · outbound

This paper cites Distributed repre- sentations of words and phrases and their compositionality.

THeGCN: Temporal Heterophilic Graph Convolutional Network Distributed repre- sentations of words and phrases and their compositionality

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.288670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c586bc73-121a-43d0-8248-e5b143d68786 · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Geom-GCN: Geometric Graph Convolutional Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.917574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7135020-04c8-4f3d-a803-f7a7d15b335b · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.921705Z

Source-reported events for the cited work

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Observation cf1d0cf0-e3af-4fc1-973d-e0d2fb77e95f · outbound

This paper cites Dysat: Deep neural rep- resentation learning on dynamic graphs via self-attention networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Dysat: Deep neural rep- resentation learning on dynamic graphs via self-attention networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.267473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4ecc7b9-2408-4ed1-bc00-61709b48e149 · outbound

This paper cites Dyrep: Learn- ing representations over dynamic graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Dyrep: Learn- ing representations over dynamic graphs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.257973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb8ea34d-fd2a-4797-8a4d-2c6874813330 · outbound

This paper cites Graph Attention Networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Graph Attention Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.933282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16872ea-5f22-489e-9d0e-ff55d67e9e4f · outbound

This paper cites How Powerful are Spectral Graph Neural Networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network How Powerful are Spectral Graph Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.937126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7aaaf9a-192e-4589-a9ef-f2dd6fb1f4e1 · outbound

This paper cites Streaming graph neural networks via con- tinual learning.

THeGCN: Temporal Heterophilic Graph Convolutional Network Streaming graph neural networks via con- tinual learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.248838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98840c26-447c-44c8-a168-76391c716106 · outbound

This paper cites Apan: Asynchronous propagation attention network for real-time temporal graph embedding.

THeGCN: Temporal Heterophilic Graph Convolutional Network Apan: Asynchronous propagation attention network for real-time temporal graph embedding

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.235647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cbae50af-41f2-4f90-b54d-838f0b76171f · outbound

This paper cites Rete: retrieval-enhanced temporal event forecasting on unified query product evolutionary graph.

THeGCN: Temporal Heterophilic Graph Convolutional Network Rete: retrieval-enhanced temporal event forecasting on unified query product evolutionary graph

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.224992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f03d4676-3662-4a88-b94b-158e523d10b6 · outbound

This paper cites Sim- plifying graph convolutional networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Sim- plifying graph convolutional networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.214356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9fa9aaa7-c417-4f63-943d-59f361ada49b · outbound

This paper cites Inductive Representation Learning on Temporal Graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Inductive Representation Learning on Temporal Graphs

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.955282Z

Source-reported events for the cited work

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Observation 71b73050-da64-4c1d-b7f8-16c2e56095ef · outbound

This paper cites From trainable negative depth to edge heterophily in graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network From trainable negative depth to edge heterophily in graphs

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.202917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7686de46-17ab-44a3-b236-c81cd7cca74b · outbound

This paper cites Link prediction based on graph neural networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Link prediction based on graph neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.180254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98733831-be3f-4f3d-b3c6-0231f94265db · outbound

This paper cites Graph Neural Networks for Graphs with Heterophily: A Survey.

THeGCN: Temporal Heterophilic Graph Convolutional Network Graph Neural Networks for Graphs with Heterophily: A Survey

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.970074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a21a132-2fc4-460a-97e9-3d848094332d · outbound

This paper cites TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.973270Z

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Observation f59ed231-7831-4866-acad-ea70cf974d09 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.168750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e2c882b-72f7-4df6-9004-1ffd12bbfd79 · outbound

This paper cites Graph neural networks with heterophily.

THeGCN: Temporal Heterophilic Graph Convolutional Network Graph neural networks with heterophily

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.157896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d3d8c43-d43f-49fa-ac6c-33d3a11bde0e · outbound

This paper cites DynGEM: Deep Embedding Method for Dynamic Graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network DynGEM: Deep Embedding Method for Dynamic Graphs

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.872260Z

Source-reported events for the cited work

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Observation e2818219-a217-4670-8c94-2ae8cf4a54d6 · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

THeGCN: Temporal Heterophilic Graph Convolutional Network Adaptive Universal Generalized PageRank Graph Neural Network

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.848254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0feacf2-ed2b-41ee-b708-cee051a63160 · outbound

This paper cites Continuous-time dynamic network embed- dings.

THeGCN: Temporal Heterophilic Graph Convolutional Network Continuous-time dynamic network embed- dings

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.278104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a22d087-b40c-414f-ba08-718e31e183c0 · outbound

This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

THeGCN: Temporal Heterophilic Graph Convolutional Network Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.887975Z

Source-reported events for the cited work

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Observation bf32da00-7616-4a75-b97e-5fe7ce2243ee · outbound

This paper cites Bernnet: Learning arbitrary graph spectral filters via bernstein approximation.

THeGCN: Temporal Heterophilic Graph Convolutional Network Bernnet: Learning arbitrary graph spectral filters via bernstein approximation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.880193Z

Source-reported events for the cited work

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Observation c5ec8fba-78f5-4b6f-b0b8-5538b26b2863 · outbound

This paper cites Inductive representation learning on large graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Inductive representation learning on large graphs

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.328955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 86194bda-4c6d-4507-8e56-a7c84522f27f · outbound

This paper cites Finding Global Homophily in Graph Neural Networks When Meeting Heterophily.

THeGCN: Temporal Heterophilic Graph Convolutional Network Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T10:38:15.895432Z

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Unavailable: canonical work link unavailable.

Observation 459b4e9d-0f17-4279-aa5f-b1410a955f67 · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

THeGCN: Temporal Heterophilic Graph Convolutional Network Beyond low-frequency information in graph convolutional networks

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.394939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91fdd3e3-2e4a-4b41-9e14-8a84ccc4148e · outbound

This paper cites Freeway perfor- mance measurement system: mining loop detector data.

THeGCN: Temporal Heterophilic Graph Convolutional Network Freeway perfor- mance measurement system: mining loop detector data

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.384142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 020c52af-65ee-4dbe-bb85-0869a7ea3fad · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily.

THeGCN: Temporal Heterophilic Graph Convolutional Network Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.362349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 128a7a2a-7a51-4420-ab9d-5b4c2b6248ab · outbound

This paper cites Roland: graph learning framework for dynamic graphs.

THeGCN: Temporal Heterophilic Graph Convolutional Network Roland: graph learning framework for dynamic graphs

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:38:16.191854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 700e97ed-69cd-4e04-a2fb-3a6533d191c9 · inbound

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction cites this paper.

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction THeGCN: Temporal Heterophilic Graph Convolutional Network

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T20:18:20.158523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 772ae33a-63f9-4afb-bd24-6d3d0176b90b · inbound

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence cites this paper.

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence THeGCN: Temporal Heterophilic Graph Convolutional Network

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T23:49:14.760975Z

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Unavailable: canonical work link unavailable.

Observation b832d4ac-f511-4c7e-babe-82d3ffe72cda · inbound

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting cites this paper.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting THeGCN: Temporal Heterophilic Graph Convolutional Network

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:00.361040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86e5c2eb-f2ab-4dca-975d-da965463acee · inbound

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation cites this paper.

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation THeGCN: Temporal Heterophilic Graph Convolutional Network

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:31:22.301309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.