Vital spreaders identification in complex networks with multi-local dimension

Wen, Tao; Pelusi, Danilo; Deng, Yong

doi:10.1016/j.knosys.2020.105717

Cited by 71 publications

(25 citation statements)

References 82 publications

(63 reference statements)

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“…Liu et al [198] proposed a generalized weighted gravity model, called generalized mechanics model (GMM), by considering global information and local information. Wen et al [199] proposed multi-local dimension (MLD) based on the fractal property to identify vital spreaders in complex networks. MLD is considered as a more general method, such as some existing centrality metrics.…”

Section: ) Influence Maximizationmentioning

confidence: 99%

A Survey on Centrality Metrics and Their Network Resilience Analysis

et al. 2021

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Centrality metrics have been studied in the network science research. They have been used in various networks, such as communication, social, biological, geographic, or contact networks under different disciplines. In particular, centrality metrics have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed from 1940s, only some centrality metrics (e.g., degree, betweenness, or cluster coefficient) have been commonly in use. This paper aims to introduce various existing centrality metrics and discusses their applicabilities in various networks. In addition, we conducted extensive simulation study in order to demonstrate and analyze the network resilience of targeted attacks using the surveyed centrality metrics under four real network topologies. We also discussed algorithmic complexity of centrality metrics surveyed in this work. Through the extensive experiments and discussions of the surveyed centrality metrics, we encourage their use in solving various computing and engineering problems in networks.

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Section: ) Influence Maximizationmentioning

confidence: 99%

A Survey on Centrality Metrics and Their Network Resilience Analysis

et al. 2021

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“…Liu et al [25] proposed a GMM model, which combines the local information and global information of the network to identify the most influential nodes in the social network. Wen et al [26] proposed multi-local dimension (MLD) method to identify the vital spreader in the social network and found that the node with low MLD value would be more important in the network. On the other hand, heuristic algorithms based on greedy mechanisms mainly include CELF [27], CELF++ [28], etc.…”

Section: Related Workmentioning

confidence: 99%

Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

et al. 2021

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The top-k influential individuals in a social network under a specific topic play an important role in reality. Identifying top-k influential nodes of a social network is still an open and deeply-felt problem. In recent years, some researchers adopt the swarm intelligence algorithm to solve such problems and obtain competitive results. There are two main algorithm models for swarm intelligence, namely Ant Colony System (ACS) and Particle Swarm Optimization (PSO). The discretized basic Particle Swarm Algorithm (DPSO) shows comparable performance in identifying top-k influential nodes of a social network. However, the performance of the DPSO algorithm is directly related to the choice of its local search strategy. The local search strategy based on greedy mechanism of the initial DPSO can easily lead to the global suboptimal solution due to the premature convergence of the algorithm. In this paper, we adopt the degree centrality based on different neighbourhoods to enhance its local search ability. Through experiments, we find that local search strategies based on different neighbourhoods have significant differences in the improvement of the algorithm's global exploration capabilities, and the enhancement of the DPSO algorithm based on the degree centrality of different neighbourhoods has a saturation effect. Finally, based on the degree centrality of the best neighbourhood with improved local search ability, we propose the DPSO_NDC algorithm. Experimental results in six real-world social networks show that the proposed algorithm outperforms the initial DPSO algorithm and other state-of-the-art algorithms in identifying the top-k influence nodes.

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“…There is a thriving interest in the study of games, 1 which has diverse applications in areas such as communications, networking, economics, and control theory 2‐8 . Computer science and artificial intelligence (AI) are no exception.…”

Section: Introductionmentioning

confidence: 99%

Exploring the effects of computational costs in extensive games via modeling and simulation

et al. 2021

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Game theory has become a standard tool for depicting and demonstrating various game‐like phenomena by providing appropriate mathematical models and for analyzing and predicting agents' behaviors and their decisions by formalizing solution concepts. The conventional game model mainly concerns ideal systems that would always guarantee optimal responses, which appears unrealistic for practical game scenarios since decision‐making usually entails resource costs. Therefore, this study considers players' decision‐making in extensive games when the computational cost of searching the strategy space is limited. We start with a new mathematical model of extensive games that features a bound on computational resources during players' decision‐making process such that they can only foresee a part of the available alternatives in the future. This model is more appropriate in predicting players' strategies than the conventional model, under which we investigate the effects of computational costs on players' strategies as well as the computational complexity. Furthermore, a simulation experiment is performed to seek the connection between the amount of resources and the goodness of the outcomes. This study is expected to provide a foundation for players' rational decision‐making with computational costs.

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Vital spreaders identification in complex networks with multi-local dimension

Cited by 71 publications

References 82 publications

A Survey on Centrality Metrics and Their Network Resilience Analysis

A Survey on Centrality Metrics and Their Network Resilience Analysis

Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

Exploring the effects of computational costs in extensive games via modeling and simulation

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