Abstract

Random graphs have gained popularity as simple and sparse models of many complex real-world networks such as the Internet, gene regulatory networks, social networks etc. Their simplicity facilitates the analysis of interactions on complex networks, which can be modelled as stochastic processes. In these lectures, we will briefly review some of the common random graph models and some of their structural properties. We will then go on to consider some applications such as routing, gossip-based information dissemination, and epidemic spread.

Speaker Bio

Ayalvadi Ganesh received his BTech in EE from IIT Madras in 1988, MS and PhD in EE from Cornell University in 1991 and 1995 respectively. His Ph.D. thesis was on the use of large deviation techniques in queueing theory. He was with Edinburgh University, Birkbeck College, London, U.K., and Hewlett-Packards Basic Research Institute in Mathematical Sciences (BRIMS) and Microsoft Research before joining the Mathematics Department of Bristol University. He was also a Fellow of Kings College, Cambridge, from 2000 to 2004.

He has published extensively on Queueing Theory and Large Deviations, Bayes' Asymptotics, Economics of Communication Networks, Peer-to-peer Systems and Algorithms, Random graphs and stochastic processes on graphs, and Computer Viruses and Worms. He is the coauthor, with Neil O'Connell and Damon Wischik, of the Springer Book "Big Queues" published in 2004.

His research interests are in the mathematical modelling of communication and computer networks, and in decentralised algorithms for such networks. Specific interests include large deviations and applications to queueing theory and statistics, random graph models and stochastic processes on graphs, and decentralised algorithms for resource allocation in the Internet and in wireless networks.