| Paper ID | MLSP-38.1 |
| Paper Title |
GraphNet: Graph Clustering with Deep Neural Networks |
| Authors |
Xianchao Zhang, Jie Mu, Han Liu, Xiaotong Zhang, Dalian University of Technology, China |
| Session | MLSP-38: Neural Networks for Clustering and Classification |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Existing deep graph clustering methods usually rely on neural language models to learn graph embeddings. However, these methods either ignore node feature information or fail to learn cluster-oriented graph embeddings. In this paper, we propose a novel deep graph clustering framework to tackle these two issues. First, we construct a feature transformation module to effectively integrate node feature information with graph topologies. Second, we introduce a graph embedding module and a self-supervised learning strategy to constrain graph embeddings by leveraging the graph similarity and the self-learning loss to group similar graphs together, thus encouraging the obtained graph embeddings to be cluster-oriented. Extensive experimental results on eight real-world graph datasets validate the superiority of the proposed method over existing ones. |
