Influence Maximization in Real-World Closed Social Networks

TL;DR

This paper addresses influence maximization in privacy-preserving closed social networks by proposing an efficient network augmentation method to enhance information spread, validated through real-world experiments.

Contribution

It introduces a novel approach to influence maximization in closed networks by augmenting the diffusion network with selected edges, improving influence spread.

Findings

The proposed method significantly outperforms baseline approaches.

It is computationally efficient and scalable to large networks.

Experimental results confirm improved influence spread in real-world networks.

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.