Journal Article10.1016/J.KNOSYS.2020.106600
Competitive and complementary influence maximization in social network: A follower’s perspective
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TL;DR: A Competitive and Complementary Independent Cascade diffusion model is proposed, and a novel optimization problem, follower-based influence maximization that aims to select top-K influential nodes as seed nodes, which can maximize the influence of a social network where multiple competitive and complementary products have already been propagated is proposed.
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Abstract: The problem of influence maximization is to select a small set of seed users in a social network to maximize the spread of influence. Recently, this problem has attracted considerable attention due to its applications in both commercial and social fields, such as product promotion, contagion prevention and public opinion forecasting. Most of prior work focuses on the diffusion model of single propagating entity, purely-complementary entities or purely-competitive entities. However, in reality, the influence diffusion in the social network is certainly more general, involving multiple propagating entities, which are competitive or complementary rather than single entity, purely-complementary entities or purely-competitive entities. In this paper, we consider the problem that a company (follower) intends to promote a new product into the market by maximizing the influence of a social network, where multiple competitive and complementary products have been spreading. We propose a Competitive and Complementary Independent Cascade (CCIC) diffusion model, and propose a novel optimization problem, follower-based influence maximization that aims to select top-K influential nodes as seed nodes, which can maximize the influence of a social network where multiple competitive and complementary products have already been propagated. To solve follower-based influence maximization problem, we propose a Deep Recursive Hybrid model (DRH) and an approximation algorithm (DRHGA). The DRH model dynamically tracks entity correlations, cascade correlations, causalities between ratings and next-period adoption through a deep recursive network and computes influence probabilities between nodes on target product. Then, with the influence probabilities predicted through DRH model, the DRHGA algorithm can efficiently find the seed node set for the target product under the CCIC diffusion model. Experimental results conducted on several public datasets show that our method outperforms the state-of-the-art methods on prediction accuracy and efficiency.
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Citations
Targeted influence maximization in competitive social networks
TL;DR: Zhang et al. as mentioned in this paper proposed a reverse reachable set-based greedy (RRG) algorithm to solve the targeted influence maximization in competitive social networks (TIMC) problem and theoretically proved its approximation ratio.
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A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
TL;DR: In this paper , the authors review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the influence maximization problem and other variants in social networks.
Influence maximization considering fairness: A multi-objective optimization approach with prior knowledge
Hao Gong,Chunxiang Guo +1 more
TL;DR: In this article , the influence maximization problem (IMP) considering fairness (FIMP), a new definition of fairness is introduced, which can correctly evaluate the fairness of a seed set, and then, the FIMP is modeled as a multi-objective optimization problem and addressed by FIMMOGA.
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Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data
Weihan Li,D.S. Liu,Yang Li,Ming Liang Hou,Jie Liu,Zhen Zhao,Aibin Guo,Huimin Zhao,Wu Deng +8 more
TL;DR: The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification, enhancing the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.
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Balanced influence maximization in social networks based on deep reinforcement learning
Shuxin Yang,Quanming Du,Guixiang Zhu,Jie Cao,Lei Chen,Weiping Qin,Youquan Wang +6 more
TL;DR: A Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module that can accurately evaluate the impact of entity correlation on information propagation.
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