Summary of Grasp: Simple Yet Effective Graph Similarity Predictions, by Haoran Zheng et al.
GraSP: Simple yet Effective Graph Similarity Predictions
by Haoran Zheng, Jieming Shi, Renchi Yang
First submitted to arxiv on: 13 Dec 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 Graph similarity computation (GSC) is a fundamental problem in the graph community, with applications in various areas. Two important metrics for GSC are graph edit distance (GED) and maximum common subgraph (MCS), both of which are NP-hard to compute exactly. Recent solutions have resorted to using graph neural networks (GNNs) to learn data-driven models for estimating GED and MCS. However, most existing approaches involve node-level interactions crossing graphs, leading to high computation overhead but limited effectiveness. This paper presents GraSP, a simple yet effective approach for predicting GED and MCS. GraSP achieves high result efficacy through several key instruments: enhanced node features via positional encoding, a GNN model augmented by a gating mechanism, residual connections, and multi-scale pooling. Theoretically, GraSP can surpass the 1-WL test, indicating its high expressiveness. Empirically, extensive experiments comparing GraSP against 10 competitors on multiple benchmark datasets demonstrate its superiority in terms of both effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraSP is a new way to compare graphs by finding their similarities. This method uses special kinds of artificial intelligence called graph neural networks (GNNs) to learn how to compare graphs. GNNs are good at looking at patterns in data, like graphs. GraSP has several key parts that make it work well: it makes the nodes on the graph more special by adding extra information, it uses a special way of combining this information with other things, and it looks at the graph at different scales to find more similarities. This method is very good at finding similarities between graphs and can do it quickly. |
Keywords
» Artificial intelligence » Gnn » Positional encoding
