Structural Clustering for Bipartite Graphs

Bipartite graphs are widely used in many real-world applications, where discovering clusters is crucial for understanding their underlying structure. However, most existing clustering methods for bipartite graphs enforce the assignment of all vertices to clusters, often neglecting the important roles of outliers and hubs. To address this limitation, we plan to extend the structural clustering model from unipartite to bipartite graphs. This extension is non-trivial due to the lack of common neighbors in bipartite graphs, which renders traditional similarity measures less effective. Recognizing that similarity is key to structural clustering, we resort to butterflies—the fundamental building blocks of bipartite graphs—to define a more effective similarity measure. Building on this, we further propose a novel structural clustering model, SBC, tailored for bipartite graphs. To enable clustering under this model, we develop efficient online and index-based methods, along with a dynamic maintenance method to accommodate graph updates over time. Extensive experiments on real-world bipartite graphs demonstrate that: (1) The SBC model greatly enhances clustering quality, achieving higher modularity while effectively identifying outliers and hubs. (2) Our proposed clustering methods are highly scalable, enabling the processing of graphs with up to 12.2 million edges within 2 seconds