OLCPM: An online framework for detecting overlapping communities in dynamic social networks
نویسندگان
چکیده
منابع مشابه
Detecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
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A community can be defined as a subset of the users in a social network that is more tightly interconnected than the overall network. Communities are useful, for instance, to guide information dissemination and acquisition, to recommend or introduce people who would likely benefit from direct interaction, and to express access control policies. In this paper, we study algorithms for automatical...
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There are some critical privacy concerns in the current online social networks (OSNs). Users' information is disclosed to different entities that they were not supposed to access. Furthermore, the notion of friendship is inadequate in OSNs since the degree of social relationships between users dynamically changes over the time. Additionally, users may define similar privacy settings for their f...
متن کاملOverlapping Communities in Social Networks
Complex networks can be typically broken down into groups or modules. Discovering this “community structure” is an important step in studying the large-scale structure of networks. Many algorithms have been proposed for community detection and benchmarks have been created to evaluate their performance. Typically algorithms for community detection either partition the graph (nonoverlapping commu...
متن کاملOverlapping communities in social networks
Identifying communities is essential for understanding the dynamics of a social network. The prevailing approach to the problem of community discovery is to partition the network into disjoint groups of members that exhibit a high degree of internal communication. This approach ignores the possibility that an individual may belong to two or more groups. Increasingly, researchers have begun to e...
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ژورنال
عنوان ژورنال: Computer Communications
سال: 2018
ISSN: 0140-3664
DOI: 10.1016/j.comcom.2018.04.003