IM2Vec: Representation learning-based preference maximization in geo-social networks

Jin, Ziwei; Shang, Jiaxing; Ni, Wancheng; Zhao, Liang; Liu, Dajiang; Qiang, Baohua; Xie, Wu; Min, Geyong

doi:10.1016/j.ins.2022.04.062

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Then, they propose an ML-based variant, NN-Sower, that uses randomization and deep learning to improve efficiency with a slight loss of quality. Jin et al [84] want to incorporate the user preferences and improve the efficiency in location-based IM problem. They propose a new framework, IM2Vec, that includes a representation learning model, All2Vec, to capture user user preferences and applies the technique of reverse influence sampling to get an efficient approximation algorithm with (1 − 1/𝑒 − 𝜀) approximation.…”

Section: Location-aware Influence Maximizationmentioning

confidence: 99%

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

Li¹,

Gao²,

Yunxuan³

et al. 2022

Preprint

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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.

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Section: Location-aware Influence Maximizationmentioning

confidence: 99%