# Influence Maximization in Real-World Closed Social Networks

arXiv: https://arxiv.org/abs/2209.10286 · HTML: https://mlsanity.com/paper/2209.10286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition](https://mlsanity.com/paper/2405.13707/) (2024)
- [An Efficient and Scalable Graph Condensation with Structure-Preserving](https://mlsanity.com/paper/2605.31016/) (2026)

## Abstract (arXiv)

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.