In this paper, we investigate the problem of influence seeding strategy in multilayer networks. In consideration of the fact that there exist inter-layer conversion costs associated with influence diffusion between layers in multiplex networks, a novel two-step seeding strategy is proposed to identify influential individuals in multiplex networks. The first step is to determine the target layer, and the second step is to identify the target seeds. Specifically, we first propose two comparable layer selection strategies, namely, multiplex betweenness centrality and multi-hop multiplex neighbors (MMNs), to determine the target layer of seeding diffusion and then construct a multiplex gravity centrality (MGC) in the manner of the gravity model to identify the influential seeds in the target layer. Subsequently, we employ a redefined independent cascade model to evaluate the effectiveness of our proposed seeding strategy by comparing it with other commonly used centrality indicators, which is validated on both synthetic and real-world network datasets. The experimental results indicate that our proposed seeding strategy can obtain greater influence coverage. In addition, parameter analysis of a neighborhood range demonstrates that MMN-based target layer selection is relatively robust, and a smaller value of a neighborhood range can enable MGC to achieve better influence performance.
We study the problem of universal resilience patterns in complex networks against cascading failures. We revise the classical betweenness method and overcome its limitation of quantifying the load in cascading model. Considering that the generated load by all nodes should be equal to the transported one by all edges in the whole network, we propose a new method to quantify the load on an edge and construct a simple cascading model. By attacking the edge with the highest load, we show that, if the flow between two nodes is transported along the shortest paths between them, then the resilience of some networks against cascading failures inversely decreases with the enhancement of the capacity of every edge, i.e. the more capacity is not always better. We also observe the abnormal fluctuation of the additional load that exceeds the capacity of each edge. By a simple graph, we analyze the propagation of cascading failures step by step, and give a reasonable explanation of the abnormal fluctuation of cascading dynamics.
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