Suman Kundu is an Assistant Professor of the Department of Computer Science and Engineering at Indian Institute of Technology Jodhpur. He leads the research group SoNAA: Social Network Analysis and Application. The research group is part of Cognitive and Social Analytics Lab of the department of Computer Science and Engineering at IIT Jodhpur. Dr. Kundu received his Ph.D. degree from Jadavpur University in 2017. His doctoral research work was carried out at Center for Soft Computing Research, Indian Statistical Institute between 2010 and 2015. He visited Engine group at Wroclaw University of Science and Technology in 2018-2019 for his postdoctoral research.
Dr. Kundu has more than 6 years of industrial software development experience with Zinfi Software Systems Pvt. Ltd., Kolkata. He published several articles in the area of social network analysis, granular computing, soft computing. His research interests includes social network analysis, network science, soft computing, crowd sourcing, fuzzy and rough set, and granular computing.
PhD in Social Network Analysis, 2017
Center for Soft Computing Research, Indian Statistical Insitute (Degree awarded by Jadavpur University)
ME in Software Engineering, 2009
Jadavpur University, Kolkata
BTech in Information Technology, 2005
Netaji Subhash Engineering College, Kolkata
A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top-k most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects.
The paper describes a new approach of viewing a social relation as a string with various forces acting on it. Accordingly, a tension measure for a relation is defined. Various component forces of the tension measure are identified based on the structural information of the network. A new variant of rough set, namely, double bounded rough set is developed in order to define these forces mathematically. It is revealed experimentally with synthetic and real-world data that positive and negative tension characterize, relatively, the presence and absence of a physical link between two nodes. An algorithm based on tension measure is proposed for link prediction. Superiority of the algorithm is demonstrated on nine real-world networks which include four temporal networks. The source code for calculating tension measure and link prediction algorithm is publicly available at My GitLab.
Community detection in a social network is a well-known problem that has been studied in computer science since early 2000. The algorithms available in the literature mainly follow two strategies, one, which allows a node to be a part of multiple communities with equal membership, and the second considers a disjoint partition of the whole network where a node belongs to only one community. In this paper, we proposed a novel community detection algorithm which identifies fuzzy-rough communities where a node can be a part of many groups with different memberships of their association. The algorithm runs on a new framework of social network representation based on fuzzy granular theory. A new index viz. normalized fuzzy mutual information, to quantify the goodness of detected communities is used. Experimental results on benchmark data show the superiority of the proposed algorithm compared to other well known methods, particularly when the network contains overlapping communities.