Analysis of identification methods of key nodes in transportation network

The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

Privacy preserving secure expansive aggregation with malicious node identification in linear wireless sensor networks

The Wireless Sensor Networks(WSNs)used for the monitoring applications like pipelines carrying oil,water,and gas;perimeter surveillance;border monitoring;and subway tunnel monitoring form linearWSNs.Here,the infrastructure being monitored inherently forms linearity(straight line through the placement of sensor nodes).Therefore,suchWSNs are called linear WSNs.These applications are security critical because the data being communicated can be used for malicious purposes.The contemporary research of WSNs data security cannot fit in directly to linear WSN as only by capturing few nodes,the adversary can disrupt the entire service of linear WSN.Therefore,we propose a data aggregation scheme that takes care of privacy,confidentiality,and integrity of data.In addition,the scheme is resilient against node capture attack and collusion attacks.There are several schemes detecting the malicious nodes.However,the proposed scheme also provides an identification of malicious nodes with lesser key storage requirements.Moreover,we provide an analysis of communication cost regarding the number of messages being communicated.To the best of our knowledge,the proposed data aggregation scheme is the first lightweight scheme that achieves privacy and verification of data,resistance against node capture and collusion attacks,and malicious node identification in linear WSNs.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.