Summary of Thegcn: Temporal Heterophilic Graph Convolutional Network, by Yuchen Yan et al.
THeGCN: Temporal Heterophilic Graph Convolutional Network
by Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Xiaoting Li, Zhe Xu, Zhichen Zeng, Lihui Liu, Zhining Liu, Hanghang Tong
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenges of graph neural networks (GNNs) in addressing heterophily issues in both spatial and temporal domains. Specifically, it focuses on static heterophilic graphs and event-based continuous graphs that exhibit edge heterophily and temporal heterophily simultaneously. The authors introduce a novel metric for measuring temporal edge heterophily and propose the Temporal Heterophilic Graph Convolutional Network (THeGCN) model to capture both spatial and temporal heterophily. THeGCN consists of a sampler and an aggregator that select relevant events and encode node attributes, edge attributes, and temporal information into node embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing problems in computer programs called Graph Neural Networks (GNNs). GNNs are great at processing data that looks like a graph or network. But sometimes this data has “edge heterophily” which means the connections between things are different. The authors of this paper want to solve this problem and also another one where the connections change over time. They come up with a new way to measure these problems and create a special program called THeGCN that can handle both types of issues. |
Keywords
» Artificial intelligence » Convolutional network
