Sequential network node importance mining method and device

Abstract

The invention discloses a sequential network node importance mining method and device; the method firstly builds the sequential network according to interaction time relations between N nodes, and then slices the sequential network according to preset time window sizes, thus obtaining sliced network sequence; the sliced network sequence comprises the following steps: for m slice networks matched with m time windows, a weight value is respectively calculated for all connecting edges of each slice network; weight value accumulation is respectively carried out according to different connecting edges for weight values corresponding to all connecting edges in each slice network, thus obtaining the weight accumulated value of the connecting edge between paired nodes in the sequential network; the weight accumulated value of the connecting edge between each paired nodes is updated to the sequential network, thus obtaining the weighted network; finally using the connecting edge containing the weight accumulated value to calculate importance indexes of each node in the weighted network, wherein the importance indexes of each node comprise the sum of weight accumulated values corresponding to all connecting edges of the corresponding nodes.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for mining the importance of nodes in a time series network. Background technique [0002] In the traditional study of complex networks, the method often used is to use a large amount of original data to construct a static network, and then conduct research and analysis on the static network. Take the microblog network as an example. In the microblog network, each user is set as a node in the network, and the mutual interaction between microblog users is represented by the connection between nodes. For example, a user Actions such as liking, reposting, and commenting on microblogs posted by users they follow are regarded as interactive behaviors between microblog users. A static network is constructed through the interaction between users in the microblog network. [0003] An important direction of complex network research is the study of the centrality of...

AI Technical Summary

Problems solved by technology

But in the real situation, after node c transmits the information to node a, it cannot pass the information to node b, because the connection edge of node pair (a, b) is generated before node c transmits the information to node a of

From the above analysis, it can be seen that the static network in the prior art cannot fit the actual real network well

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