Identification on Spatio-temporal Characteristics of the Traffic of Communication System Based on Coupling Bipartite Network
TAN Guimin1, WANG Lina1,2, ZANG Chenrui3
1. College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China; 2. Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot 010051, China; 3. Inner Mongolia Branch, China Unicom, Hohhot 010050, China
Abstract：In order to identify the spatio-temporal characteristics of the traffic of communication system, Network model and core-periphery model is used to analyze the traffic data. Considering both the temporal information and the spatial information, we construct a data-voice coupled bipartite network. The edge weight distribution of the temporal network is lognormal. The maximal connected subgraph of the spatial network is denoted as S-network and it is a small world network. The edge weight of the S-network obeys power-law distribution. The frequency of coupling events in core is higher than that in periphery. The core-periphery model can effectively identifies spatio-temporal areas where data-voice coupling events occur intensively.
谭桂敏, 汪丽娜, 臧臣瑞. 耦合二分网络识别通信系统流量的时空特征[J]. 复杂系统与复杂性科学, 2022, 19(2): 71-79.
TAN Guimin, WANG Lina, ZANG Chenrui. Identification on Spatio-temporal Characteristics of the Traffic of Communication System Based on Coupling Bipartite Network[J]. Complex Systems and Complexity Science, 2022, 19(2): 71-79.
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