An Enhanced Community Detection Metric for Weighted and Directed Graph-Based Network

Kabir, Md. Hasibul; Jahan, Md. Anower

dc.contributor.author	Kabir, Md. Hasibul
dc.contributor.author	Jahan, Md. Anower
dc.date.accessioned	2021-10-06T04:50:22Z
dc.date.available	2021-10-06T04:50:22Z
dc.date.issued	2017-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/1092
dc.description	Supervised by Dr. Abu Raihan Mostofa Kamal, Associate Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	Community detection algorithm tries to find the densely connected units in a large network. For this objective different matrices have come into light. Most of them assume all the vertices in a community belong equally to the community. But these matrices identify the communities as whole, these doesn’t give any information at which content the nodes are connected in a community. Moreover these also face resolution limit for larger networks. For resolving this issue, another matrices named permanence has been applied. But this metric is not defined for weighted and directed graph. Our approach will be to implement the permanence on directed and weighted graph.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	An Enhanced Community Detection Metric for Weighted and Directed Graph-Based Network	en_US
dc.type	Thesis	en_US