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

• URL: http://arxiv.org/abs/2211.03074v1
• Date: Sun, 6 Nov 2022 10:13:42 GMT
• Title: A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
• Authors: Yandi Li, Haobo Gao, Yunxuan Gao, Jianxiong Guo, Weili Wu
• Abstract summary: Influence Maximization (IM) is a classical optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. Main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research.
• Score: 2.9882027965916413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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