Proceedings of the 15th international conference on World Wide Web
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Exploring Local Community Structures in Large Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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One common problem in viral marketing, counter-terrorism and epidemic modeling is the efficient detection of a community that is centered at an individual of interest. Most community detection algorithms are designed to detect all communities in the entire network. As such, it would be computationally intensive to first detect all communities followed by identifying communities where the individual of interest belongs to, especially for large scale networks. We propose a community detection algorithm that directly detects the community centered at an individual of interest, without the need to first detect all communities. Our proposed algorithm utilizes an expanding ring search starting from the individual of interest as the seed user. Following which, we iteratively include users at increasing number of hops from the seed user, based on our definition of a community. This iterative step continues until no further users can be added, thus resulting in the detected community comprising the list of added users. We evaluate our algorithm on four social network datasets and show that our algorithm is able to detect communities that strongly resemble the corresponding real-life communities.