Summary of Edge Classification on Graphs: New Directions in Topological Imbalance, by Xueqi Cheng et al.
Edge Classification on Graphs: New Directions in Topological Imbalance
by Xueqi Cheng, Yu Wang, Yunchao Liu, Yuying Zhao, Charu C. Aggarwal, Tyler Derr
First submitted to arxiv on: 17 Jun 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 Recent advancements in Graph machine learning (GML) have led to remarkable success in node/graph classification and link prediction. However, the edge classification task has not seen significant progress despite its numerous real-world applications in social network analysis and cybersecurity. To address this gap, our study proposes a comprehensive approach to edge classification by introducing Topological Entropy (TE), a novel metric that measures topological imbalance for each edge. We demonstrate that prioritizing edges with high TE values can help mitigate the issue of topological imbalance through strategies like Topological Reweighting and TE Wedge-based Mixup. Our proposed strategy, TopoEdge, integrates these approaches and achieves better performance on newly curated datasets, establishing a new benchmark for (imbalanced) edge classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to sort edges in a network into different categories based on their characteristics. This task is called edge classification, but it’s been difficult because the data is often imbalanced – some classes have many more examples than others. Researchers have made progress in other areas of graph machine learning, but this issue has been holding them back from making similar advancements in edge classification. To overcome this challenge, scientists developed a new metric called Topological Entropy (TE) that measures how unbalanced the data is for each edge. They used this metric to create strategies like rewighting and mixing up the data to prioritize edges with high TE values. The result is a new approach called TopoEdge that can help classify edges more accurately. |
Keywords
* Artificial intelligence * Classification * Machine learning
