Summary of An Efficient Loop and Clique Coarsening Algorithm For Graph Classification, by Xiaorui Qi et al.
An Efficient Loop and Clique Coarsening Algorithm for Graph Classification
by Xiaorui Qi, Qijie Bai, Yanlong Wen, Haiwei Zhang, Xiaojie Yuan
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a novel approach to graph-level tasks using Graph Transformers (GTs). While existing GT-based works focus on node-central perspectives, this research explores explicit representations of edges and structures. The authors introduce an efficient algorithm, Loop and Clique Coarsening (LCC4GC), which leverages hierarchical heuristic graph coarsening and well-designed constraints to learn high-level interactions between structures. LCC4GC is applied to GT architecture for Graph Classification tasks, showcasing improvements over 31 baselines from various architectures on eight real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a complex network of connections, like social media relationships or internet links. Existing methods just focus on individual nodes (people or websites), but what about the patterns and structures in how they connect? This paper explores a new way to analyze these networks using “Graph Transformers” and proposes an efficient method to capture both node and edge information. The results show that this approach outperforms other methods on eight real-world datasets. |
Keywords
» Artificial intelligence » Classification
