Introduction to algorithms
Computational organization theory
Computational organization theory
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Algorithm Design
ACM SIGKDD Explorations Newsletter
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Locating hidden groups in communication networks using hidden Markov models
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Multi-scale dynamics in a massive online social network
Proceedings of the 2012 ACM conference on Internet measurement conference
Visualizing the evolution of community structures in dynamic social networks
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Mining most frequently changing component in evolving graphs
World Wide Web
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We propose two approximation algorithms for identifying communities in dynamic social networks. 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. Recently, we have proposed an optimization-based framework for modeling dynamic community structure. Also, we have proposed an algorithm for finding such structure based on maximum weight bipartite matching. In this paper, we analyze its performance guarantee for a special case where all actors can be observed at all times. In such instances, we show that the algorithm is a small constant factor approximation of the optimum. We use a similar idea to design an approximation algorithm for the general case where some individuals are possibly unobserved at times, and to show that the approximation factor increases twofold but remains a constant regardless of the input size. This is the first algorithm for inferring communities in dynamic networks with a provable approximation guarantee. We demonstrate the general algorithm on real data sets. The results confirm the efficiency and effectiveness of the algorithm in identifying dynamic communities.