Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised protein classification using cluster kernels
Bioinformatics
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
A tutorial on spectral clustering
Statistics and Computing
Spectral clustering in telephone call graphs
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Symmetrizations for clustering directed graphs
Proceedings of the 14th International Conference on Extending Database Technology
Proceedings of the 20th international conference on World wide web
Fragile online relationship: a first look at unfollow dynamics in twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Alternate views of graph clusterings based on thresholds: a case study for a student forum
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
GeoRank: an efficient location-aware news feed ranking system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Many social networks, e.g., Slashdot and Twitter, can be represented as directed graphs (digraphs) with two types of links between entities: mutual (bi-directional) and one-way (uni-directional) connections. Social science theories reveal that mutual connections are more stable than one-way connections, and one-way connections exhibit various tendencies to become mutual connections. It is therefore important to take such tendencies into account when performing clustering of social networks with both mutual and one-way connections. In this paper, we utilize the dyadic methods to analyze social networks, and develop a generalized mutuality tendency theory to capture the tendencies of those node pairs which tend to establish mutual connections more frequently than those occur by chance. Using these results, we develop a mutuality-tendency-aware spectral clustering algorithm to identify more stable clusters by maximizing the within-cluster mutuality tendency and minimizing the cross-cluster mutuality tendency. Extensive simulation results on synthetic datasets as well as real online social network datasets such as Slashdot, demonstrate that our proposed mutuality-tendency-aware spectral clustering algorithm extracts more stable social community structures than traditional spectral clustering methods.