A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Abstract

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance.With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out. CCS Concepts: • Networks → Network algorithms; • Theory of computation → Design and analysis of algorithms; • Computing methodologies → Machine learning approaches; Knowledge representation and reasoning.

Fair Influence Maximization in Large-scale Social Networks Based on Attribute-aware Reverse Influence Sampling

“…• Second, recently graph neural networks (GNNs) have become more and more popular in dealing with NP-hard graph based problems (Ranjan, Grover, Medya, Chakravarthy, Sabharwal, & Ranu, 2022;Bai, Xu, Sun, & Wang, 2021). Even though many works have tried to propose deep learning methods (Li, Gao, Gao, Guo, & Wu, 2022;Khajehnejad et al, 2021) for influence maximization related problems, there is still great potential in solving fair influence maximization through GNN models.…”

Graph Bayesian Optimization for Multiplex Influence Maximization

“…Influence Maximization. The influence maximization (IM) problem was introduced in seminal work by (Kempe, Kleinberg, and Tardos 2003), relying on diffusion models like Linear Threshold and Independent Cascade (Li et al 2018(Li et al , 2022a. Much research has developed traditional simulation, proxy, and sketch-based methods (Goyal, Lu, and Lakshmanan 2011;Borgs et al 2014;Tang, Shi, and Xiao 2015), as well as recent learning-based techniques (Ling et al 2023).…”

“…This interest has led to the study of influence maximization (IM), an optimization problem that aims to maximize the influence spread within a specific diffusion model by selecting a limited number of seeds (Kempe, Kleinberg, and Tardos 2003). IM has garnered considerable attention from both industry and academia due to its relevance in various realworld applications, including viral marketing, epidemic con- trol, and rumor blocking (Li et al 2018;Zhang et al 2022;Li et al 2022a).…”

Modeling and Mitigating Online Misinformation: a Suggested Blockchain Approach

“…It should be noted that the studies on containing misinformation have been intertwined with the ones on influence maximization. Li et al [35] compile a recent and comprehensive survey on influence maximization using deep learning methods. Comprehensive reviews of recent misinformation detection and containment techniques are given in [53,68,70].…”