GMM: A generalized mechanics model for identifying the importance of nodes in complex networks

Liu, Fan; Wang, Zhen; Deng, Yong

doi:10.1016/j.knosys.2019.105464

Cited by 91 publications

(31 citation statements)

References 56 publications

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“…The Lhc is compared with other eight well-known measures involving Local, Global and Semi-local metrics from the aspects of discriminability, correctness and robustness. The methods are DC (degree centrality) [ 14 ], BC (betweenness centrality) [ 15 ], H-index method(H-index) [ 19 ], LC (local centrality) [ 22 ], Cnc + (neighborhood coreness) [ 23 ], G + (extended gravity index) [ 26 ] and EW(extended weight degree centrality) [ 25 ] and LGM(local version of the gravity model) [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…Stating that the node's influence is not limited up to the nearest neighbors level preferably, the gravity centrality [26] and the local gravity model take into account both neighborhood information and path information to evaluate the node's influence [27]. The generalized mechanics model enrich it by combining the global information and local information [28]. Considering that the community structure [29] is one common and important structural properties in realworld networks, several measures [30][31][32] take advantage of the community structure to quantify the influence of nodes, such as, the combination of the number and sizes of communities to which a node directly links [30], and the combination of eintra-community and inter-community links [32].…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

et al. 2021

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Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

et al. 2021

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“…Identifying influential nodes in complex networks has been substantially studied by improving existing centrality metrics or taking more comprehensive approaches. 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.…”

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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“…Zhao et al [24] provides GIN (global importance of each node) model to identify influential nodes from the global perspective of the complex networks. 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.…”

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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GMM: A generalized mechanics model for identifying the importance of nodes in complex networks

Cited by 91 publications

References 56 publications

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

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

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