Summary of Sign Is Not a Remedy: Multiset-to-multiset Message Passing For Learning on Heterophilic Graphs, by Langzhang Liang et al.
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
by Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu, Shirui Pan, Yuan Qi
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to Graph Neural Networks (GNNs) called Multiset to Multiset GNN (M2M-GNN), designed to address limitations in Signed Message Passing (SMP) for heterophilic graphs. Specifically, M2M-GNN tackles undesirable representation updates and oversmoothing issues that can occur with SMP. Theoretical analyses and experiments demonstrate the effectiveness of M2M-GNN in outperforming SMP on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to make Graph Neural Networks (GNNs) better for certain types of data. GNNs are powerful tools, but they have some problems when dealing with data where nodes don’t look similar to each other. The authors show that a common technique called Signed Message Passing has its own limitations and propose a new way to do things called Multiset to Multiset GNN (M2M-GNN). This new method seems to work better than the old one in some cases. |
Keywords
» Artificial intelligence » Gnn
