A framework for community identification in dynamic social networks Export

@inproceedings{citeulike:1603536, abstract = {We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimization-based approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NP-hard and APX-hard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples.}, address = {New York, NY, USA}, author = {Tantipathananandh, Chayant and Wolf, Tanya B. and Kempe, David}, booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {1603536}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1281192.1281269}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1281192.1281269}, doi = {10.1145/1281192.1281269}, isbn = {978-1-59593-609-7}, keywords = {analysis, community, detection, network}, location = {San Jose, California, USA}, pages = {717--726}, posted-at = {2009-07-01 13:14:55}, priority = {2}, publisher = {ACM}, title = {A framework for community identification in dynamic social networks}, url = {http://dx.doi.org/10.1145/1281192.1281269}, year = {2007} }

TY - CONF ID - citeulike:1603536 L3 - citeulike-article-id:1603536 N2 - We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimization-based approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NP-hard and APX-hard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples. CY - San Jose, California, USA SN - 978-1-59593-609-7 AD - New York, NY, USA EP - 726 TI - A framework for community identification in dynamic social networks PB - ACM T2 - KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 717 KW - analysis KW - community KW - detection KW - network AU - Tantipathananandh, Chayant AU - Wolf, Tanya AU - Kempe, David PY - 2007/// UR - http://dx.doi.org/10.1145/1281192.1281269 ER -