Community Detection for Social Network Analysis

By Seema Rani

Speedy growth of communication on social media as a result of stress-free and inexpensive means of communication has persuaded to hook up with people across the globe. The communication among people irrespective of place and time leads to the unprecedented growth of social networks. The availability and accessibility of wide ranging networks have opened up many research avenues for researchers' from multidiscipline. In the present time, the presence of networks in multidiscipline and the demand for their analysis have taken the social network analysis research to astounding scales. One endeavor of immense significance in social networks is "Community Detection". A community in real-world networks plays a persuasive role in understanding the functionality and organization of complex systems. In real-world networks, members communicate based on likes or dislikes, common or disparate interest etc. Accordingly, they share their membership with one or more groups in the network. Based on the high density of member with in a subgroup, the network is partitioned into subgroups. Thus, the community detection problem can be stated as "identifying highly cohesive connected subgroups in the networks". Community detection problem is witnessed from traditional and optimization problem viewpoint. The communities present in networks often have an obvious interpretation. They accelerate the understanding of the functional organization of the networks. More recently, the focus is on introducing effective and improved community detection algorithms. Primarily, the output of the community detection algorithm for network partitions is evaluated by extensively adopted measure, modularity. In social network analysis, finding network partitions with maximum modularity is an NP-hard problem. Thus, the problem is articulated as an optimization problem. Evolutionary algorithms to deal with optimization problem are one among the prevalent choices. In the last decade, many new nature-inspired algorithms have been introduced and applied for solving different optimization problems.

• Format: Paperback
• Language: English
• ISBN: B09TMSBMSX
• ISBN13: 9798210052452
• Release Date: February 2022
• Publisher: Blurb
• Length: 132 Pages
• Weight: 0.43 lbs.
• Dimensions: 0.3" x 6.1" x 9.2"