Cai Gao

A bio-inspired methodology of identifying influential nodes in complex networks.

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

A new closeness centrality measure via effective distance in complex networks

Closeness centrality (CC) measure, as a well-known global measure, is widely applied in many complex networks. However, the classical CC presents many problems for flow networks since these networks are directed and weighted. To address these issues, we propose an effective distance based closeness centrality (EDCC), which uses effective distance to replace conventional geographic distance and binary distance obtained by Dijkstras shortest path algorithm. The proposed EDCC considers not only the global structure of the network but also the local information of nodes. And it can be well applied in directed or undirected, weighted or unweighted networks. Susceptible-Infected model is utilized to evaluate the performance by using the spreading rate and the number of infected nodes. Numerical examples simulated on four real networks are given to show the effectiveness of the proposed EDCC.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

A Bio-Inspired Algorithm for Route Selection in Wireless Sensor Networks

How to determine the optimal communication path in wireless sensor networks (WSNs) is a fundamental problem. In this letter, we formulate the optimal communication path problem and convert it into the shortest path tree (SPT) problem by considering an external base station and sensors as the root node and leaf nodes, respectively. Inspired by a path-finding mathematical model Physarum solver, a novel bio-inspired algorithm is proposed to solve the SPT problem in WSNs. Experimental results demonstrate that the proposed algorithm also has an advantage of adaptivity and performs better than Physarum solver in dynamic small WSNs.

Slime Mould Inspired Applications on Graph-Optimization Problems

Since the appearance of slime mould-inspired network design applications, it has attracted the attention of many researchers from all over the world. In this chapter, we provide an overview of a variety of slime mould-inspired applications on graph-optimization problems. We will focus more on the mathematical model inspired by slime mould, develop a novel Energy Propagation model, and also covers its applications to many graph optimization problems. Some examples of such applications include Shortest Path Tree Problem (SPT), Supply Chain Network Design (SCNP), Maze Problem and Multi-source Multi-sink Minimum Cost Flow Problem.