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BGAE: Auto-Encoding Multi-View Bipartite Graph Clustering
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DOI：10.1109/TKDE.2024.3363217.png)
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Abstract
En 中文
Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale 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 auto-encoding 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.
Keywords:
Bipartite graph
Decoding
Machine learning
Encoding
Synthetic data
Matrix decomposition
Image reconstruction
Bipartite graph learning
encoding-decoding
graph machine learning
multi-view clustering
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10.4
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6.7K
Citations:
3.2W
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