document: A framework for community identification in dynamic social networks 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 .
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