Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Keyword search on external memory data graphs
Proceedings of the VLDB Endowment
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Spectral Clustering with Random Projection and Sampling
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Fast large-scale spectral clustering by sequential shrinkage optimization
ECIR'07 Proceedings of the 29th European conference on IR research
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast spectral clustering of data using sequential matrix compression
ECML'06 Proceedings of the 17th European conference on Machine Learning
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Graph clustering has received growing attention in recent years as an important analytical technique, both due to the prevalence of graph data, and the usefulness of graph structures for exploiting intrinsic data characteristics. However, as graph data grows in scale, it becomes increasingly more challenging to identify clusters. In this paper we propose an efficient clustering algorithm for large-scale graph data using spectral methods. The key idea is to repeatedly generate a small number of "supernodes" connected to the regular nodes, in order to compress the original graph into a sparse bipartite graph. By clustering the bipartite graph using spectral methods, we are able to greatly improve efficiency without losing considerable clustering power. Extensive experiments show the effectiveness and efficiency of our approach.