Network community discovery: solving modularity clustering via normalized cut
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Pattern change discovery between high dimensional data sets
Proceedings of the 20th ACM international conference on Information and knowledge management
Quadratic nonnegative matrix factorization
Pattern Recognition
Exploiting homophily effect for trust prediction
Proceedings of the sixth ACM international conference on Web search and data mining
Exploring temporal effects for location recommendation on location-based social networks
Proceedings of the 7th ACM conference on Recommender systems
A sparse nonnegative matrix factorization technique for graph matching problems
Pattern Recognition
A continuous characterization of the maximum-edge biclique problem
Journal of Global Optimization
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Nonnegative matrix factorization (NMF) is a versatile model for data clustering. In this paper, we propose several NMF inspired algorithms to solve different data mining problems. They include (1) multi-way normalized cut spectral clustering, (2) graph matching of both undirected and directed graphs, and (3) maximal clique finding on both graphs and bipartite graphs. Key features of these algorithms are (a) they are extremely simple to implement; and (b) they are provably convergent. We conduct experiments to demonstrate the effectiveness of these new algorithms. We also derive a new spectral bound for the size of maximal edge bicliques as a byproduct of our approach.