Summary of Multi-order Graph Clustering with Adaptive Node-level Weight Learning, by Ye Liu et al.
Multi-order Graph Clustering with Adaptive Node-level Weight Learning
by Ye Liu, Xuelei Lin, Yejia Chen, Reynold Cheng
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a new approach to graph clustering, called Multi-Order Graph Clustering (MOGC), which integrates multiple higher-order structures and edge connections at the node level. The current state-of-the-art methods focus on individual nodes and edges, neglecting the organization of motifs, which are recurring patterns in graphs. MOGC addresses this limitation by employing an adaptive weight learning mechanism to adjust the contributions of different motifs for each node. This approach tackles the hypergraph fragmentation issue that plagues previous higher-order clustering methods, resulting in enhanced clustering accuracy. The algorithm is efficiently solved using an alternating minimization algorithm and is evaluated on seven real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MOGC is a new way to group nodes in graphs based on patterns they follow together. Usually, these groups are formed by looking at individual connections between nodes or edges. But MOGC looks at higher-level patterns called motifs that appear many times in the graph. It adjusts how much each motif contributes to forming groups for each node, which helps solve a problem where previous methods split into smaller groups. This approach is better because it considers more information and works well on real-world graphs. |
Keywords
» Artificial intelligence » Clustering
