BGAE: Auto-encoding Multi-view Bipartite Graph Clustering (Extended Abstract)

Liang Li, Yuangang Pan, Jie Liu, Yue Liu, Xinwang Liu, Kenli Li, Ivor W. Tsang, Keqin Li

Abstract: With the rapid growth of multimodal and multi-view data, multi-view bipartite graph clustering (MVBGC) has emerged as a promising solution for large-scale tasks, which with linear complexity. However, most methods adhere to a unidirectional “encoding” design, where the bipartite graph is directly constructed from input data. Enlightened by the prevalent encoding-decoding in deep learning, this paper rethinks existing paradigms and proposes a novel “auto-encoding” MVBGC framework, named BGAE. Our model seamlessly integrates encoding, bipartite graph learning, and decoding modules within a self-supervised learning framework. The encoding module extracts a joint representation from input data, the bipartite graph learning module learns a discriminative bipartite graph in latent semantic space, and the decoding module reconstructs the input data by the structural information. Extensive experiments verify the superiority of our novel design, particularly highlighting the critical role of “decoding” learning. This work represents the first attempt to explore encoding-decoding design in MVBGC.

External IDs:dblp:conf/icde/LiPLLLLTL25