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- research-articleApril 2024
ProtoMGAE: Prototype-Aware Masked Graph Auto-Encoder for Graph Representation Learning
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 137, Pages 1–22https://doi.org/10.1145/3649143Graph self-supervised representation learning has gained considerable attention and demonstrated remarkable efficacy in extracting meaningful representations from graphs, particularly in the absence of labeled data. Two representative methods in this ...
- ArticleSeptember 2023
Contrastive Learning with Cluster-Preserving Augmentation for Attributed Graph Clustering
Machine Learning and Knowledge Discovery in Databases: Research TrackSep 2023, Pages 644–661https://doi.org/10.1007/978-3-031-43412-9_38AbstractGraph contrastive learning has attracted considerable attention and made remarkable progress in node representation learning and clustering for attributed graphs. However, existing contrastive-based clustering methods separate the processes of ...
- research-articleJuly 2023
Deep embedded clustering with distribution consistency preservation for attributed networks
Highlights- A distribution consistency preserving deep embedded clustering model is proposed.
- The model exploits GAE and AE to learn node representations and clusters jointly.
- A consistency constraint is designed to maintain the consistency of ...
Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much ...
- research-articleDecember 2019
A generative model for exploring structure regularities in attributed networks
Information Sciences: an International Journal (ISCI), Volume 505, Issue CDec 2019, Pages 252–264https://doi.org/10.1016/j.ins.2019.07.084AbstractMany real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this ...