Bivas Mitra is currently working as a postdoctoral researcher at French National Centre for Scientific Research (CNRS), Paris. He received a PhD in Computer Science and Engineering from Indian Institute of Technology, Kharagpur in 2011, after a M.Tech. from IIT Kharagpur in 2003 and B.Tech. from Haldia Institute of Technology, Vidyasagar University in 2001 both in Computer Science and Engineering. From 2003 to 2006, he worked as a lecturer in the department of Computer Science and Engineering at Haldia Institute of Technology. He also worked at Soffront Software (India) Pvt. Ltd. as a Software Engineer in 2001. In his PhD tenure, he received various fellowships like national doctoral fellowship, SAP Labs India doctoral fellowship etc. and several student travel grants to participate in different international conferences. His research interests include complex networks, social networks, peer-to-peer networks, networks modeling, optical networks, wireless internet etc.

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Expert Audience

English

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Saarbrücken

Understanding the dynamics in large scale networks is a major challenge in front of the network research community. Traditional graph theoretic approaches have their own limitations and are not applicable due to the large size and dynamic nature of the network. In this background, my talk primarily addresses two different issues in technological and social networks. The first half of my talk is directed towards understanding the resilience and emergence of technological networks, specifically superpeer networks. We propose an analytical framework in order to measure the resilience of superpeer networks in face peer churn and attacks. Other side, it is not obvious why bootstrapping of peer nodes and other local dynamics results in the appearance of bimodal degree distribution in superpeer networks like Gnutella. We develop a formalism which enables us to explain the emergence of bimodal network in face of dynamics like peer bootstrapping, churn, link rewiring etc. Further analysis leads us in formulating interesting bootstrapping protocols such that superpeer network evolves with desired topological properties.

The second half of my talk mostly focuses towards the detection and analysis of dynamical communities in social networks, specifically in citation network. Most of the recent methods aim at exhibiting community partitions from successive graph snapshots and thereafter connecting or smoothing these partitions using clever time-dependent features and sampling techniques. These approaches are nonetheless achieving "longitudinal" rather than 'dynamic' community detection. Assuming that communities are fundamentally defined by a certain amount of interaction recurrence among a possibly disparate set of nodes over time, we suggest that the loss of information induced by considering successive snapshots makes it difficult to appraise essentially dynamic phenomena. We propose a methodology which aims at tackling this issue in the context of citation datasets, and present several illustrations on both empirical and synthetic dynamic network datasets.

Vera Laubscher

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