BGAE: Auto-Encoding Multi-View Bipartite Graph Clustering

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

Abstract:

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in largescale
tasks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly
from the input data, i.e. only consider the unidirectional “encoding” process. However, “encoding-decoding” mechanism is a popular
design for deep learning, the most representative one is auto-encoder (AE). Enlightened by this, this paper rethinks existing MVBGC
paradigms and transfers the “encoding-decoding” design into graph machine learning, and proposes a novel framework termed autoencoding multi-view bipartite graph clustering (BGAE), which integrates encoding, bipartite graph construction, and decoding modules in a self-supervised learning manner. The encoding module extracts a latent joint representation from the input data, the bipartite graph construction module learns a bipartite graph with connectivity constraint in latent semantic space, and the decoding module recreates the input data via the bipartite graph. Therefore, our novel BGAE combines representation learning, bipartite graph learning, reconstruction learning, and label inference into a unified framework. All the modules are seamlessly integrated and mutually reinforcing for clustering-friendly purposes. Extensive experiments verify the superiority of our novel design and the significance of “decoding” process. To the best of our knowledge, this is the first attempt to explore “encoding-decoding” design in traditional MVBGC. The code is provided at https://github.com/liliangnudt/BGAE.