Summary of Ecgn: a Cluster-aware Approach to Graph Neural Networks For Imbalanced Classification, by Bishal Thapaliya et al.
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
by Bishal Thapaliya, Anh Nguyen, Yao Lu, Tian Xie, Igor Grudetskyi, Fudong Lin, Antonios Valkanas, Jingyu Liu, Deepayan Chakraborty, Bilel Fehri
First submitted to arxiv on: 15 Oct 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 A novel approach to graph classification is proposed in this paper, which tackles two key challenges: adapting to class imbalances and leveraging clustering structure information. The Enhanced Cluster-aware Graph Network (ECGN) integrates cluster-specific training with synthetic node generation to address these issues. Unlike traditional Graph Neural Networks (GNNs), ECGN learns different aggregations for different clusters, utilizing the clusters to generate new minority-class nodes that clarify the inter-class decision boundary. By combining cluster-aware embeddings with a global integration step, ECGN enhances the quality of resulting node embeddings, outperforming its closest competitors by up to 11% on widely studied benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to classify nodes in graphs. It tries to solve two big problems: dealing with uneven class sizes and using information about how groups are clustered together. The new method is called Enhanced Cluster-aware Graph Network (ECGN). ECNG combines two techniques: making the same node update process different for each cluster, and creating fake minority-class nodes that help explain the decision boundary between classes. This approach works with any existing graph neural network and clustering technique. The results show that ECNG performs better than other methods by up to 11% on some common datasets. |
Keywords
» Artificial intelligence » Classification » Clustering » Graph neural network
