Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Simple ingredients leading to very efficient heuristics for the maximum clique problem
Journal of Heuristics
On Effectively Finding Maximal Quasi-cliques in Graphs
Learning and Intelligent Optimization
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The community-search problem and how to plan a successful cocktail party
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On triangulation-based dense neighborhood graph discovery
Proceedings of the VLDB Endowment
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Densest subgraph in streaming and MapReduce
Proceedings of the VLDB Endowment
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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A great deal of research has been conducted on modeling and discovering communities in complex networks. In most real life networks, an object often participates in multiple overlapping communities. In view of this, recent research has focused on mining overlapping communities in complex networks. The algorithms essentially materialize a snapshot of the overlapping communities in the network. This approach has three drawbacks, however. First, the mining algorithm uses the same global criterion to decide whether a subgraph qualifies as a community. In other words, the criterion is fixed and predetermined. But in reality, communities for different vertices may have very different characteristics. Second, it is costly, time consuming, and often unnecessary to find communities for an entire network. Third, the approach does not support dynamically evolving networks. In this paper, we focus on online search of overlapping communities, that is, given a query vertex, we find meaningful overlapping communities the vertex belongs to in an online manner. In doing so, each search can use community criterion tailored for the vertex in the search. To support this approach, we introduce a novel model for overlapping communities, and we provide theoretical guidelines for tuning the model. We present several algorithms for online overlapping community search and we conduct comprehensive experiments to demonstrate the effectiveness of the model and the algorithms. We also suggest many potential applications of our model and algorithms.