A New Ranking Technique to Enhance the Infection Size in Complex Networks

Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, between and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the nodes. A new technique is proposed in this research to develop the K-Shell metric by using the degree of the node, and a coreness of its rounding friends to estimate the ability of the node in spreading the infection within the network. The experimental results, which were done on four types of real and synthetic networks, and using an epidemic propagation model SIR, demonstrate that the suggested technique can measure the node effect more precisely and offer a unique ordering group than other centrality measures

Computer vision algorithms for 3D object recognition and orientation: a bibliometric study

This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.This research was funded by the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Congestion occurs and propagates in the stations of urban rail transit, which results in the impendent need to comprehensively evaluate the station performance. Based on complex network theory, a key station identification method is considered. This approach considers both the topology and dynamic operation states of urban rail transit network, such as degree, passenger demand, system capacity and capacity utilization. A case of Beijing urban rail transit is applied to verify the validation of the proposed method. It shows that the method can be helpful to daily passenger flow control and capacity enhancement during peak hours.</p

A Survey on Centrality Metrics and Their Implications in Network Resilience

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they 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 for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure