Transport processes are universal in real-world complex networks, such as communication and transportation networks. As the increase of transport demands in these complex networks, the problems of traffic congestion and transport delay become more and more serious, which call for a systematic network transport optimization. However, it is pretty challenging to improve transport capacity and efficiency simultaneously, since they are often contradictory in that improving one degenerates the other. In this paper, we formulate a multi-objective optimization problem including two objectives: maximizing the transport capacity and minimizing the average number of hops. In this problem, we explore the optimal edge weight assignments and the associated routing paths, corresponding to the optimal trade-off between the two objectives. To solve this problem, we provide a multi-objective evolutionary algorithm, namely network centrality guided multi-objective particle swarm optimization (NC-MOPSO). Specifically, within the framework of MOPSO, we propose a hybrid population initialization mechanism and a local search strategy by employing the network centrality theory to enhance the quality of initial solutions and strengthen the exploration of the search space, respectively. Simulation experiments performed on network models and real networks show that our algorithm has better performance than five state-of-the-art alternatives on several most-used metrics.

In modern power grids, a local failure or attack can trigger catastrophic cascading failures, which makes it challenging to assess the attack vulnerability of power grids. In this Brief, we define the optimal K-link attack problem and study the attack vulnerability of power grids in cascading failures. We propose a link centrality measure based on both topological and electrical properties for power grids. According to this centrality measure, we propose a greedy attack algorithm and an optimal attack algorithm. Simulation results based on standard IEEE bus test data show that the optimal attack is better than the greedy attack and the traditional PSO-based attack in fracturing power grids. Moreover, the greedy attack has an adequate attack efficiency with a smaller computational cost. Our work aids in understanding the vulnerability of power grids and provides clues for securing power grids.

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In a previous work [Xu et al. Physica A, 456 294 (2016)], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices.

For many power-limited networks, such as wireless sensor networks and mobile ad hoc networks, maximizing the network lifetime is the first concern in the related designing and maintaining activities. We study the network lifetime from the perspective of network science. In our model, nodes are initially assigned a fixed amount of energy moving in a square area and consume the energy when delivering packets. We obtain four different traffic regimes: no, slow, fast, and absolute congestion regimes, which are basically dependent on the packet generation rate. We derive the network lifetime by considering the specific regime of the traffic flow. We find that traffic congestion inversely affects network lifetime in the sense that high traffic congestion results in short network lifetime. We also discuss the impacts of factors such as communication radius, node moving speed, routing strategy, etc., on network lifetime and traffic congestion.