A new hierarchical clustering algorithm to identify non-overlapping like-minded communities

DOI

https://doi.org/10.1145/2914586.2914613
Final published version

Talasila Sai Deepak
Hindol Adhya
Shyamal Kejriwal
Bhanuteja Gullapalli
Saswata Shannigrahi

In this paper, we present a new algorithm to identify non- overlapping like-minded communities in a social network and compare its performance with Girvan-Newman algorithm, Lovain method and some well-known hierarchical clustering algorithms on Twitter and Filmtipset datasets.

Original language	English
Title of host publication	HT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media
Publisher	Association for Computing Machinery
Pages	319-321
Number of pages	3
ISBN (Electronic)	9781450342476
DOIs	https://doi.org/10.1145/2914586.2914613
State	Published - 10 Jul 2016
Event	27th ACM Conference on Hypertext and Social Media, HT 2016 - Halifax, Canada Duration: 10 Jul 2016 → 13 Jul 2016

Publication series

Name	HT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media

Conference

Conference	27th ACM Conference on Hypertext and Social Media, HT 2016
Country/Territory	Canada
City	Halifax
Period	10/07/16 → 13/07/16

Scopus subject areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software
Artificial Intelligence

Research areas

Community detection, Like-mindedness, Modularity

ID: 49849008