Summary of Graph As Point Set, by Xiyuan Wang et al.
Graph as Point Set
by Xiyuan Wang, Pan Li, Muhan Zhang
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel approach to learning graph representations bijectively transforms interconnected nodes into a set of independent points, enabling the use of set encoders and expanding the design space of Graph Neural Networks (GNNs). The proposed graph-to-set conversion method also allows for principled injection of graph information into Transformer architectures. A Point Set Transformer (PST) architecture is introduced, which accepts a point set converted from a graph as input and exhibits superior expressivity for short-range substructure counting and long-range shortest path distance tasks compared to existing GNNs. Extensive experiments validate PST’s outstanding real-world performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn about connections between things (called a graph). Instead of using special networks called Graph Neural Networks, it converts the graph into a set of independent points and uses another type of network to learn from those points. This approach has two main benefits: it lets us use new types of networks that were previously not possible, and it allows us to add information about the connections between things to existing networks. The paper also presents a special type of network called Point Set Transformer, which is better at some tasks than previous networks. |
Keywords
» Artificial intelligence » Transformer
