Time sequence network modeling method and important node identification method

Abstract

The invention discloses a sequential network modeling method based on interlayer coupling relationship intensity attenuation, which comprises the following steps of dividing the number of layers of a network, and defining an intra-layer relationship and an interlayer relationship of a multilayer graph sequential network model; calculating a coupling relationship between corresponding node layers in the two time layer networks based on a local similarity index; calculating an attenuation factor of interlayer coupling strength, and calculating an interlayer coupling relation; calculating an adjacent matrix corresponding to the plurality of time layer networks, and representing an intra-layer connection relationship; using an interlayer coupling relationship to represent an interlayer connection relationship; and obtaining a super-adjacency matrix of which the interlayer coupling strength is attenuated through the intra-layer connection relationship and the interlayer connection relationship, and modeling the sequential network through the obtained super-adjacency matrix of which the interlayer coupling strength is attenuated. The method can be suitable for time sequence networks of different structures, the calculation complexity is lower than that of a global method, meanwhile, the important nodes in the time sequence network can be more effectively recognized, and the significant application value is achieved in the important node recognition research field of the time sequence network.

Description

technical field [0001] The invention relates to a method for identifying the importance of nodes in a time-series network, in particular to a method for modeling a time-series network based on the attenuation of the strength of interlayer coupling relationships, and reflects the importance of nodes based on an eigenvector centrality index. Background technique [0002] Online social networks play an important role in people's lives. People can express ideas, share information, and influence each other through social networks. Identifying important nodes in the network has important applications in advertising, public opinion monitoring, and recommendation systems. [0003] After retrieving domestic and foreign literature, it is found that most of the current methods for identifying important nodes are based on static networks. However, many networks in real life cannot be simply modeled as static networks, because the nodes in the network may only be in a certain There are...

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Problems solved by technology

[0007] In view of the lack of accuracy of the important node identification method in the existing complex network, the present invention provides a time series network modeling method and an important node identification method based on the attenuation of the interlayer coupling relationship strength, which can be applied to time series networks of different structures, There are no adjustable parameters in actual use. While the computational complexity is lower than that of the global method, it can more effectively identify important nodes in the time series network, and has significant application value in the field of important node identification research in time series networks.

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