An incremental approach for hierarchical community mining in evolving social graphs

Keshab Nath, Swarup Roy and Sukumar Nandi

International Journal of Intelligent Enterprise, 2021, vol. 8, issue 2/3, 123-141

Abstract: Community members which are highly connected with each other inside a community tends to create sub-communities, commonly termed as intrinsic or hierarchical communities. Finding intrinsic communities help us to reach out specific user needs, understanding the network dynamics and unveiling the functional and hidden aspects in the network, which is difficult without unveiling intra and inter-community all kinds of relationship. With the passage of time, members of a community may acquire different interests, leads to movement of members within different communities. Frequent changes in the relationship of members towards a community make the task of community detection even more challenging. In this work, we propose a new community detection method, embedded communities from evolving networks (ECEnet), for handling intrinsic communities in evolving networks. We adopt a density variation concept to detect the intrinsic communities in growing networks. We use a new membership function to measure the contiguity of a member towards a community. We use both synthetic and real-world social networks for our experimentation. Experimental results reveal that ECEnet is successful in detecting intrinsic or hierarchical communities in a dynamic scenario.

Keywords: intrinsic communities; hierarchical communities; evolving networks; dynamic communities; incremental clustering; embedded cluster; density variation. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=114496 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijient:v:8:y:2021:i:2/3:p:123-141

Access Statistics for this article

More articles in International Journal of Intelligent Enterprise from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().