Abstract
Most complex real systems are found to have multiple layers of connectivity and required to be modelled as multiplex networks. One of the extremely critical problems is to reduce the congestion and enhance the transfer capacity, especially in real communication networks with a big data environment. A novel and effective strategy to improve traffic and control congestion is proposed by adding edges according to their weights which are defined by the topology structural properties. Furthermore, which layer is more effective when our strategy is applied is discussed based on its topology structure. Adding edges between nodes whose product of multiplex network betweenness is the highest is confirmed to be more effective, particularly in the layer with stronger community structure. Simulation experiments on both computer-generated and real-world networks demonstrate that our strategy can enhance the transfer capacity of multiplex networks significantly, which is in good agreement with our analysis.
Highlights
Traditional studies of complex networks usually assume that all nodes are linked to each other by a certain type of edge to produce a single-layer network
It has been recognized that lots of complex real systems are not composed by single network primitively, but by multiplex network [17,18,19,20,21,22,23,24]. ey consist of a series of N nodes linked by L different kinds of interactions
We generate a multiplex network that only consists of two Erdős–Renyi single-layer networks
Summary
Traditional studies of complex networks usually assume that all nodes are linked to each other by a certain type of edge to produce a single-layer network. E multiplex network consists of two or more single-layer networks with different community structures. By adding edges in the light of the different definitions of edge weight, we present strategies to enhance the transfer capacity of the multiplex network.
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