Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
To address these issues, we propose a conjoint framework CoCo, which captures compactness and consistency in the learned node representations for deep graph clustering.
ProofPaper ↗
Key points
- Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters.
- This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph topology for effective cluster assignments.
- Technically, our CoCo leverages graph convolutional filters to learn robust node representations from both local and global views, and then encodes them into low-rank compact embeddings, thus effectively removing the redundancy and noise as well as uncovering the intrinsic underlying structure.
- To further enrich the node semantics, we develop a consistency learning strategy based on compact embeddings to facilitate knowledge transfer from the two perspectives.
Sources (1)
- [1]Compactness and Consistency: A Conjoint Framework for Deep Graph ClusteringarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:43 AM
To address these issues, we propose a conjoint framework CoCo, which captures compactness and consistency in the learned node representations for deep graph clustering.
Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters.
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