PhD Seminar / Colloquium Presentation Announcement of Ali Abbasi Tadi:"NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks"

 

 

Date: Friday November 4, 2022 

Time: 11:00am – 12:00pm  

Location: Erie Hall, Room 3123 

Reminder: Two part attendance mandatory, arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.

 

Discovering clusters in social networks is of fundamental and practical interest. This talk presents a novel clustering strategy for large-scale highly connected social networks. We propose a new hybrid clustering technique based on non-negative matrix factorization and independent component analysis for finding complex relationships among users of a huge social network. We extract the important features of the network and then perform clustering on independent and important components of the network. Moreover, we introduce a new k-means centroid initialization method by which we achieve higher efficiency. We apply our approach on four well-known social networks: Facebook, Twitter, Academia and YouTube. We experimentally show that our approach achieves much better results in terms of the Silhouette coefficient compared to well-known counterparts such as Hierarchical Louvain, Multiple Local Community detection, and k-means++. 

 

Keywords: Non-negative matrix factorization, independent component analysis, k-means, Hierarchical Louvain clustering 

 

Ali is a Ph.D. candidate in the school of computer science at the university of Windsor. His main research direction is cyber security specifically privacy preserving in machine learning. He is currently working on a project for secure parallel clustering. He has published multiple conference and peer-reviewed journal papers and received multiple scholarships from the school of computer science. The other domain of his research includes privacy preserving in federated learning. He is IEEE member and the program committee of ADMA 2022 conference.  

 

Internal Reader: Dr. Rueda          

Internal Reader: Dr. Samet 

External Reader: Dr. Zhang   

Advisor: Dr. Dima Alhadidi