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An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks

Evren GŁney (guneye***at***mef.edu.tr)

Abstract: The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art (1-1/e) bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks

Keywords: Influence maximization, binary integer programming, sample average approximation, pipage method

Category 1: Combinatorial Optimization (Approximation Algorithms )

Category 2: Integer Programming (0-1 Programming )

Category 3: Network Optimization

Citation: Technical Report -TRIE1801- MEF University, Department of Industrial Engineering, 2018/07

Download: [PDF]

Entry Submitted: 10/25/2018
Entry Accepted: 10/25/2018
Entry Last Modified: 06/20/2019

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