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Efficient Algorithms For Analyzing large scale Network dynamics: Centrality, Community and Predictability

Research Area:  Mobile Ad Hoc Networks

Abstract:

   "Large scale networks are an indispensable part of our daily life; be it biological network, smart grids, academic collaboration networks, social networks, vehicular networks, or the networks as part of various smart environments, they are fast becoming ubiquitous. The successful realization of applications and services over them depend on efficient solution to their computational challenges that are compounded with network dynamics.
   We propose a divide and conquer based computationally efficient algorithm that leverages the underlying network community structure for deterministic computation of betweenness centrality indices for all nodes. As an integral part of it, we also propose a computationally efficient agglomerative hierarchical community detection algorithm. Next, we propose a network structure evolution based novel probabilistic link prediction algorithm that predicts set of links occurring over subsequent time periods with higher accuracy. To best capture the evolution process and have higher prediction accuracy we propose multiple time scales with the Markov prediction model.

Name of the Researcher:  Sima Das

Name of the Supervisor(s):  Sajal K. Das

Year of Completion:  2017

University:  Missouri University

Thesis Link:   Home Page Url