FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Analysis of social metrics in dynamic networks: measuring the influence with FRINGE
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Vertex neighborhoods, low conductance cuts, and good seeds for local community methods
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
Overlapping community detection using seed set expansion
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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There has been considerable interest in improving the capability to identify communities within large collections of social networking data. However, many of the existing algorithms will compartment an actor (node) into a single group, ignoring the fact that in real-world situations people tend to belong concurrently to multiple groups. Our work focuses on the ability to find overlapping communities by aggregating the community perspectives of friendship groups, derived from egonets. We will demonstrate that our algorithm not only finds overlapping communities, but additionally helps identify key members, which bind communities together. Additionally, we will highlight the parallel feature of the algorithm as a means of improving runtime performance.