Improved modularity for community detection analysis in weighted graph

Social networks become very popular in recent years seen from the popularity of online social networks. Relationship on social network consists of connection between users that can be modeled by a graph with nodes and edges. This relation graph can form a community that is a group of nodes within which connection is dense, but between them the connection is sparse. According to this property of communities, previous research has been conducted that proposes MC modularity for measuring how good a particular division into communities based on the concept of coupling coefficient. This research adopts that modularity by adding the network weight as a variable in the formula. The community detection system in this research used Girvan-Newman algorithm with computational of MC modularity. In order to make the value of modularity still in the range of 0-1, then does the mapping function in the calculation of the MC modularity. The experimental results in the form of communities that generated through MC modularity calculation by adding weight variable into calculation has compared with communities that generated under different modularity. The mapping function in computational MC modularity on a weighted graph can generate communities that meet the property of communities. Based on the test results, this modularity can be used for detecting community with average accuracy 99.275%.

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