Characteristics of the social network of bike share members in New York City

Reilly, Kathleen H.; Wang, Shu Meir; Gould, L. Hannah; Baquero, María; Crossa, Aldo

doi:10.1016/j.jth.2021.101128

Cited by 6 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One important research direction is to rank the nodes in the network, which is of great significance for clarifying network structure and maintaining network operation. In fact, various complex systems that accompany human daily life can be expressed as complex networks, such as scientific cooperation networks, [1] transportation networks, [2] social networks [3] and protein-protein interaction networks. [4] Research shows that a small number of nodes in the network play a supporting role in the whole network.…”

Section: Introductionmentioning

confidence: 99%

An extended improved global structure model for influential node identification in complex networks

Zhu¹,

Wang²

2022

Chinese Phys. B

View full text Add to dashboard Cite

Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node’s self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.

show abstract

Section: Introductionmentioning

confidence: 99%