Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The relation between Pearson's correlation coefficient r and Salton's cosine measure
Journal of the American Society for Information Science and Technology
The Structure of the Computer Science Knowledge Network
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Defining and evaluating network communities based on ground-truth
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Overlapping community detection at scale: a nonnegative matrix factorization approach
Proceedings of the sixth ACM international conference on Web search and data mining
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
OverCite: finding overlapping communities in citation network
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Study of community in time-varying graphs has been limited to its detection and identification across time. However, presence of time provides us with the opportunity to analyze the interaction patterns of the communities, understand how each individual community grows/shrinks, becomes important over time. This paper, for the first time, systematically studies the temporal interaction patterns of communities using a large scale citation network (directed and unweighted) of computer science. Each individual community in a citation network is naturally defined by a research field -- i.e., acting as ground-truth -- and their interactions through citations in real time can unfold the landscape of dynamic research trends in the computer science domain over the last fifty years. These interactions are quantified in terms of a metric called inwardness that captures the effect of local citations to express the degree of authoritativeness of a community (research field) at a particular time instance. Several arguments to unveil the reasons behind the temporal changes of inwardness of different communities are put forward using exhaustive statistical analysis. The measurements (importance of field) are compared with the project funding statistics of NSF and it is found that the two are in sync. We believe that this measurement study with a large real-world data is an important initial step towards understanding the dynamics of cluster-interactions in a temporal environment. Note that this paper, for the first time, systematically outlines a new avenue of research that one can practice post community detection.