Dynamic Influence Maximization via Network Representation Learning

Wei, Sheng; Song, Wei; Dong, Li; Yang, Fei; Zhang, Yatao

doi:10.3389/fphy.2021.827468

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…They claim that STIM outperforms the greedy agent and can learn complex patterns hidden in evolving networks without supervised data or handcrafted strategies. Sheng et al [159] solve the dynamic IM problem by adopting embedding method DynamicTriad [226] as well as modified seed selecting algorithm in DeepIM [90]. Under the dynamic network, the embeddings would take into account the dynamics of edge-add/remove and weight-change with time, then an updating algorithm is designed to update the seed set by incorporating the change of node insertion and deletion.…”

Section: Dynamic 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.

show abstract

Section: Dynamic Influence Maximizationmentioning

confidence: 99%

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

Li¹,

Gao²,

Yunxuan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

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

Gao

Yunxuan

et al. 2023

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

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, technologies based on Machine Learning (ML) have 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.

show abstract

Dynamic Influence Maximization via Network Representation Learning

Cited by 2 publications

References 28 publications

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

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

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

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