Scalable Multi-View Clustering via Bipartite Graph Consensus Filtering

As data sources and modalities become more diverse, existing multi-view graph clustering methods face high computational complexity, hindering scalability. Bipartite graph clustering overcomes this by using anchors to build association graphs, reducing complexity to linear time. However, the quality of bipartite graphs and the neglect of higher-order correlations remain significant limitations. To tackle these challenges, we propose MCBGF, a scalable bipartite graph-based multi-view clustering method enhanced by consensus graph filtering. By integrating the original bipartite structure to counteract degradation from over-smoothing in high-order filters, MCBGF achieves robust and consistent performance. With linear time complexity, MCBGF efficiently processes large-scale datasets and consistently outperforms state-of-the-art methods in experimental evaluations. The code has been released at https://github.com/sxuHui/MCBGF.