2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-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
SessionMLSP-38: Neural Networks for Clustering and Classification
LocationGather.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
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.