Community detection based on influence power

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Overlapping Community Detection in Social Networks Based on Stochastic Simulation

Community detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on diffe...

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Data-Based Influence Maximization using Community Detection

Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network .In this work, I am proposing a new algorithm drawing inspiration from the works done in the same field by [9] and [10]. The main motive behind this work is to emulate the relationship between the fields of Community ...

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overlapping community detection in social networks based on stochastic simulation

community detection is a task of fundamental importance in social network analysis. community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. there exist a variety of methods for community detection based on diffe...

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An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks

The structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering...

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ژورنال

عنوان ژورنال: Applied Informatics

سال: 2017

ISSN: 2196-0089

DOI: 10.1186/s40535-017-0037-2