Maximizing Influence of Leaders in Social Networks

• URL: http://arxiv.org/abs/2106.06128v1
• Date: Fri, 11 Jun 2021 02:31:46 GMT
• Title: Maximizing Influence of Leaders in Social Networks
• Authors: Xiaotian Zhou and Zhongzhi Zhang
• Abstract summary: We consider the edge addition problem for the DeGroot model of opinion dynamics in a social network with $n$ nodes and $m$ edges. We show that our algorithm is efficient and effective, which scales to large networks with more than a million nodes.
• Score: 15.05158252504978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The operation of adding edges has been frequently used to the study of opinion dynamics in social networks for various purposes. In this paper, we consider the edge addition problem for the DeGroot model of opinion dynamics in a social network with $n$ nodes and $m$ edges, in the presence of a small number $s \ll n$ of competing leaders with binary opposing opinions 0 or 1. Concretely, we pose and investigate the problem of maximizing the equilibrium overall opinion by creating $k$ new edges in a candidate edge set, where each edge is incident to a 1-valued leader and a follower node. We show that the objective function is monotone and submodular. We then propose a simple greedy algorithm with an approximation factor $(1-\frac{1}{e})$ that approximately solves the problem in $O(n^3)$ time. Moreover, we provide a fast algorithm with a $(1-\frac{1}{e}-\epsilon)$ approximation ratio and $\tilde{O}(mk\epsilon^{-2})$ time complexity for any $\epsilon>0$, where $\tilde{O}(\cdot)$ notation suppresses the ${\rm poly} (\log n)$ factors. Extensive experiments demonstrate that our second approximate algorithm is efficient and effective, which scales to large networks with more than a million nodes.

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