Songyang Lao

Effects of Edge Directions on the Structural Controllability of Complex Networks

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

OACRR-PSO Algorithm for Anti-ship Missile Path Planning

In order to improve the search efficiency of path planning algorithm for anti-ship missile,the planning space is researched based on geometric principle.The geometric gradual transformation rule of operational area is revealed when fusing the concept of operational area into the process of converse path planning,hereby,the operational area cluster is proposed to be its physical carrier.By introducing the operational area cluster into particle swarm optimization(PSO) algorithm,a PSO algorithm real-time restricted by operational area cluster(OACRR-PSO) is proposed.To express the operational area cluster expediently,the polar coordinates code mode is adopted in path coding.Considering the relationship between the adjoining vectors of particle,OACRR-PSO does not update all the velocity vectors of particle simultaneously in the course of optimization,which is different from conventional PSO,but updates sequentially by adopting the strategy of sequential recursion evolution.In the course of updating particle,the operational area cluster is used to restrict the position vectors of particle in exact updating area in real-time,which reduces the search space step by step to increase the convergence velocity.Simulation results indicate that the strategy of sequential recursion evolution could improve the algorithms global search capabilities and the algorithm possesses a better convergence rate and robustness.

Identifying Influence of Nodes in Complex Networks with Coreness Centrality: Decreasing the Impact of Densely Local Connection

Ranking nodes by their spreading ability has been a fundamental issue related to many real applications, such as information and disease control. In the well-known coreness centrality measure, nodes influence is ranked only by summing all neighbors k-shell values while the effect of the topological connections among them on the nodes spreading ability are ignored. In this work, we propose an improved coreness measure by decreasing the impact of densely local connections. Comparing the results from a series of susceptible-infected-recovered simulations on real networks, we show that our improved method can rank the nodes spreading ability more accurately than other ranking measures such as k-shell, distance based method, mixed degree decomposition and coreness centrality method.

Mitigation of malicious attacks on network observation

In the modeling, controlling, and monitoring of complex networks, a fundamental problem concerns the determination and observation of the systems states by using measurements or sensors as few as possible, defined as network observation. This work aims to investigate the robustness of network observation when an approach of minimum dominating set is considered in observing a network. We first investigate the structural properties of the minimum dominating sets, e.g. how the size depends on the degree–degree correlations and how to assess the nodes importance in the malicious attacks. Then, we introduce a new measurement of robustness for network observation, and implement a hill-climbing algorithm to improve its robustness by edge rewiring. Furthermore, we propose a novel rewiring strategy, called smart rewiring, which could speed up the increment of robustness index. In comparison with previous strategy of edge rewiring, the smart rewiring has been found to be successfully useful on real-world and synthetic networks.

Link Prediction in Weighted Networks: The Role of Structure Correlation

Recently, the problem of missing link prediction in complex networks has attracted much attention. Among these studies, the weight of links and the structure weight between pairs of nodes are rarely taken into account. In this paper, we propose a method to analyze the structure correlation between pairs of nodes and apply four different arithmetic-operators to measure the accuracy of the link prediction in two real networks. The results show that the structure correlation from the global network plays an important role in the link prediction. Further experimental study shows the optimal distance of the structure correlation between pairs of nodes is the key factor, otherwise it would have the opposite effect.