Multi-view Contrastive Graph Clustering
By Erlin Pan, Zhao Kang. Neural Information Processing Systems, 2021
301 citations recorded by the source corpus.
With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of two key steps: we first filter out the undesirable high-frequency noise while preserving the graph geometric features via graph filtering and obtain a smooth representation of nodes; we then learn a consensus graph regularized by graph contrastive loss. Results on several benchmark datasets show the superiority of our method with respect to state-of-the-art approaches. In particular, our simple approach outperforms existing deep learning-based methods.
Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/dade7ce49d5bb1ec362035a2b25433e047bef9dc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Information Systems
In Statistical and Nonlinear Physics
Citations in the corpus
- Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, background): “Recently, some works investigated different contrastive learning frameworks for multi-view clustering [21, 31, 39].”