Summary of Learning Persistent Community Structures in Dynamic Networks Via Topological Data Analysis, by Dexu Kong et al.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
by Dexu Kong, Anping Zhang, Yang Li
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 deep graph clustering framework that addresses the issue of temporal consistency in dynamic community detection. The authors introduce a matrix factorization-based algorithm, MFC, which preserves node embeddings to prevent representation collapse. They also develop TopoReg, a neural network regularization technique that ensures topological similarity between inter-community structures over time intervals. The approach is tested on real-world datasets with varying numbers of communities and improves both temporal consistency and clustering accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how social networks change over time. Right now, we can’t easily tell when a community starts to form or disappear because existing methods don’t consider the flow of information between groups. The authors create a new way to group nodes in a network based on their connections and show that it works well for real-world data. This is an important step forward in understanding how networks evolve. |
Keywords
* Artificial intelligence * Clustering * Neural network * Regularization
