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Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation
http://hdl.handle.net/20.500.12678/0000004037
http://hdl.handle.net/20.500.12678/00000040371b525bd8-4321-41cf-930f-cec459c90c62
84b0f1ed-9b33-4e8a-896d-6ac71522df82
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Title | ||||||
Title | Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation | |||||
Language | en | |||||
Publication date | 2019 | |||||
Authors | ||||||
Aung, Thet Thet | ||||||
Nyunt, Thi Thi Soe | ||||||
Cho, Pyae Pyae Win | ||||||
Description | ||||||
Many types of social network are modelled asgraphs. Community detection has been an important researcharea in social graph analysis. Community detection can beviewed as an optimization problem. Nowadays, researchers usenature-inspired algorithms to solve optimization problem.Their goal is to find the optimal solution for a given problem.In this paper, nature-inspired based artificial bee colonyalgorithm with crossover and mutation is used to detectcommunity in social graphs. GraphX is built as a library onthe top of Spark by encoding graph as a collection of verticesand edges. Comparative studies describe that the proposedalgorithm and other nature-inspired algorithms can effectivelydetect the community structure on real world social graphs asother traditional community detection algorithms. | ||||||
Keywords | ||||||
artificial bee colony, community detection, graph, GraphX, spark, crossover, mutation and modularity | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/1746 | |||||
Journal articles | ||||||
IEEE 4th International Conference on Computer and Communication Systems(2019) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |