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arXiv · 2209.10286

Influence Maximization in Real-World Closed Social Networks

Shixun Huang·Wenqing Lin·Zhifeng Bao·Jiachen Sun
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Abstract

In the last few years, many closed social networks such as WhatsAPP and WeChat have emerged to cater for people's growing demand of privacy and independence. In a closed social network, the posted content is not available to all users or senders can set limits on who can see the posted content. Under such a constraint, we study the problem of influence maximization in a closed social network. It aims to recommend users (not just the seed users) a limited number of existing friends who will help propagate the information, such that the seed users' influence spread can be maximized. We first prove that this problem is NP-hard. Then, we propose a highly effective yet efficient method to augment the diffusion network, which initially consists of seed users only. The augmentation is done by iteratively and intelligently selecting and inserting a limited number of edges from the original network. Through extensive experiments on real-world social networks including deployment into a real-world application, we demonstrate the effectiveness and efficiency of our proposed method.

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Shixun Huang, Wenqing Lin, Zhifeng Bao, Jiachen Sun. 2022-09-21. Influence Maximization in Real-World Closed Social Networks. https://arxiv.org/abs/2209.10286

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