Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 23rd ACM conference on Hypertext and social media
Mining social media: key players, sentiments, and communities
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Face-to-face contacts at a conference: dynamics of communities and roles
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Communities are nodes in a network that are grouped together based on a common set of properties. While the communities and link structures are often thought to be in alignment, it may not be the case when the communities are defined using other external criterion. In this paper we provide a new way to measure the alignment. We also provide a new metric that can be used to estimate the number of communities to which a node is attached. This metric, along with degree, is used to assign a communitybased role to nodes. We demonstrate the usefulness of the community-based node roles by applying them to the influence maximization problem.