Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A parallel algorithm for enumerating all the maximal k-plexes
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
CommTracker: A Core-Based Algorithm of Tracking Community Evolution
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Automatic detection of cohesive subgroups within social hypertext: A heuristic approach
The New Review of Hypermedia and Multimedia
Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Community mining on dynamic weighted directed graphs
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
Information theoretic criteria for community detection
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Using cohesive subgroups for analyzing the evolution of the friend view mobile social network
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
SISP: a new framework for searching the informative subgraph based on PSO
Proceedings of the 20th ACM international conference on Information and knowledge management
A clustering approach using weighted similarity majority margins
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Building a social network of research institutes from information available on the web
International Journal of Networking and Virtual Organisations
A mixed graph model for community detection
International Journal of Intelligent Information and Database Systems
A sock puppet detection algorithm on virtual spaces
Knowledge-Based Systems
Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining for geographically disperse communities in social networks by leveraging distance modularity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Maximal clique enumeration for large graphs on hadoop framework
Proceedings of the first workshop on Parallel programming for analytics applications
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Recent years have seen that WWW is becoming a flourishing social media which enables individuals to easily share opinions, experiences and expertise at the push of a single button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publishing content, social network researchers and graph theory researchers are now concerned with inferring community structures by analyzing the linkage patterns among individuals and web pages. Although the investigation of community structures has motivated many diverse algorithms, most of them are unsuitable for large-scale social networks because of the computational cost. Moreover, in addition to identify the possible community structures, how to define and explain the discovered communities is also significant in many practical scenarios. In this paper, we present the algorithm ComTector(Community DeTector) which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world. This algorithm does not require any priori knowledge about the number or the original division of the communities. Because real networks are often large sparse graphs, its running time is thus O(C × Tri2), where C is the number of the detected communities and Tri is the number of the triangles in the given network for the worst case. Then we propose a general naming method by combining the topological information with the entity attributes to define the discovered communities. With respected to practical applications, ComTector is challenged with several real life networks including the Zachary Karate Club, American College Football, Scientific Collaboration, and Telecommunications Call networks. Experimental results show that this algorithm can extract meaningful communities that are agreed with both of the objective facts and our intuitions.