mlsanityData to 7 Oct 2026

THeGCN: Temporal Heterophilic Graph Convolutional Network

arXiv 2412.16435 ↗First posted 21 Dec 2024cs.LG cs.IR cs.SIcited by 6

Not on the map. Why?

Abstract (arXiv)

Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more intricate structures, encompassing (1) static heterophilic graphs encountering the edge heterophily issue in the spatial domain and (2) event-based continuous graphs in the temporal domain. State-of-the-art (SOTA) has been concurrently addressing these two lines of work but tends to overlook the presence of heterophily in the temporal domain, constituting the temporal heterophily issue. Furthermore, we highlight that the edge heterophily issue and the temporal heterophily issue often co-exist in event-based continuous graphs, giving rise to the temporal edge heterophily challenge. To tackle this challenge, this paper first introduces the temporal edge heterophily measurement. Subsequently, we propose the Temporal Heterophilic Graph Convolutional Network (THeGCN), an innovative model that incorporates the low/high-pass graph signal filtering technique to accurately capture both edge (spatial) heterophily and temporal heterophily. Specifically, the THeGCN model consists of two key components: a sampler and an aggregator. The sampler selects events relevant to a node at a given moment. Then, the aggregator executes message-passing, encoding temporal information, node attributes, and edge attributes into node embeddings. Extensive experiments conducted on 5 real-world datasets validate the efficacy of THeGCN.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 40 references that are in the map. First posted on arXiv 21 Dec 2024.

Builds on

31 of 40 references.

References · 31

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19 more
  1. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMscited by 21
  2. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  3. 2025PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignmentcited by 10
  4. 2026Subspace Alignment for Vision-Language Model Test-time Adaptationcited by 5
  5. 2026Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendationcited by 4
  6. 2025Dynamic Homophily with Imperfect Recall: Modeling Resilience in Adversarial Networkscited by 0