Algorithms for clustering data
Algorithms for clustering data
Path optimization for graph partitioning problems
Discrete Applied Mathematics - Special volume on VLSI
Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Inferring agent dynamics from social communication network
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Surveying the identification of communities
International Journal of Web Based Communities
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
CDPM: Finding and Evaluating Community Structure in Social Networks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Detecting Overlapping Community Structures in Networks
World Wide Web
Overlapped community detection in complex networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Stability of individual and group behavior in a blognetwork
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Reverse engineering an agent-based hidden Markov model for complex social systems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Detecting overlapping community structures in networks with global partition and local expansion
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Communication dynamics of blog networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Where the blogs tip: connectors, mavens, salesmen and translators of the blogosphere
Proceedings of the First Workshop on Social Media Analytics
Data-driven modeling and analysis of online social networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Finding hidden group structure in a stream of communications
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Actions speak as loud as words: predicting relationships from social behavior data
Proceedings of the 21st international conference on World Wide Web
A novel genetic algorithm for overlapping community detection
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Towards linear time overlapping community detection in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Mining query log graphs towards a query folksonomy
Concurrency and Computation: Practice & Experience
Communities and Balance in Signed Networks: A Spectral Approach
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Online search of overlapping communities
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A new overlapping clustering algorithm based on graph theory
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A separability framework for analyzing community structure
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
A link clustering based overlapping community detection algorithm
Data & Knowledge Engineering
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In this paper, we present an efficient algorithm for finding overlapping communities in social networks. Our algorithm does not rely on the contents of the messages and uses the communication graph only. The knowledge of the structure of the communities is important for the analysis of social behavior and evolution of the society as a whole, as well as its individual members. This knowledge can be helpful in discovering groups of actors that hide their communications, possibly for malicious reasons. Although the idea of using communication graphs for identifying clusters of actors is not new, most of the traditional approaches, with the exception of the work by Baumes et al, produce disjoint clusters of actors, de facto postulating that an actor is allowed to belong to at most one cluster. Our algorithm is significantly more efficient than the previous algorithm by Baumes et al; it also produces clusters of a comparable or better quality.