On the maximum weight clique problem
Mathematics of Operations Research
Introduction to algorithms
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
ICALP '92 Proceedings of the 19th International Colloquium on Automata, Languages and Programming
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Minimum Spanning Tree Based Clustering Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Discovering and Explaining Abnormal Nodes in Semantic Graphs
IEEE Transactions on Knowledge and Data Engineering
Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of distance measures for graph-based clustering of documents
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Inapproximability of maximum weighted edge biclique and its applications
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Dense subgraph mining with a mixed graph model
Pattern Recognition Letters
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A mixed graph theoretic model is proposed for finding communities in a social network. Information on the habits (shopping habits, free time activities) is considered to be known at least for part of the society. The presented model is based on applying parallelly a standard and a bipartite graph. Compared to previous methods, the introduced algorithm has the advantage of noise-tolerance and is suitable independently of the size of the clusters in the graph. Clusters in the dataset tend to form dense subgraphs in both graph models. The idea is to speed up cluster core mining by a modified MST algorithm. Noise in the dataset is defined as missing information on a person's habits. Clustering noisy data is done by using a bipartite graph and fuzzy membership functions. The proposed algorithm can be used for predicting the missing data estimated on the available information patterns. The presented mixed graph model might also be used for image processing tasks.