Summary of Co-representation Neural Hypergraph Diffusion For Edge-dependent Node Classification, by Yijia Zheng et al.
Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification
by Yijia Zheng, Marcel Worring
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Hypergraphs are crucial in modeling complex relationships between nodes and edges. Recently, edge-dependent node classification (ENC) has emerged as a practically relevant yet challenging task. In ENC, nodes can have different labels across distinct hyperedges, necessitating the representation of node-edge pairs. Current solutions rely on message passing within-edge and within-node structures as multi-input single-output functions. However, these approaches suffer from limitations: non-adaptive representation size, non-adaptive messages, and insufficient direct interactions among nodes or edges. To overcome these constraints, we propose CoNHD, a novel ENC solution that models both within-edge and within-node interactions as multi-input multi-output functions. By representing these interactions as a hypergraph diffusion process on node-edge co-representations, CoNHD can adapt to specific ENC datasets. Our extensive experiments demonstrate the effectiveness and efficiency of CoNHD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big network with lots of connections between different nodes. Sometimes, the same node can be part of different groups depending on which edge it’s connected to. This is called edge-dependent node classification, or ENC for short. To solve this problem, we created a new method called CoNHD that looks at both the edges and nodes together. Our method is better than others because it can adapt to specific problems and learn from data in a more effective way. |
Keywords
» Artificial intelligence » Classification » Diffusion
