Dynamic flow graph data vertex importance updating method and device based on random walk

Abstract

The invention relates to a dynamic flow graph data vertex importance updating method and device based on random walk, and the method comprises the steps: obtaining associated data in real time according to a time sequence, and updating dynamic flow graph data in real time; obtaining influenced vertexes and newly added vertexes in the dynamic flow graph data updating process at each moment; generating a random walk path by each vertex in the dynamic flow graph data through a preset random walk mode; calculating or updating the PageRank value of each affected vertex according to the total numberof times that the random walk path passes through each affected vertex; aggregating original vertexes of dynamic flow graph data into a super vertex, reserving all connecting edges of newly-added vertexes in the dynamic flow graph data, connecting the other ends of the connecting edges with the super vertex, and obtaining a new graph is obtained, wherein the PageRank value of each newly-added vertex is calculated or updated in the new graph through the method. Compared with the prior art, not only is the accuracy of a calculation result ensured, but also the real-time performance of calculation is ensured.

Description

technical field [0001] The invention relates to the field of updating the importance of dynamic flow graph data vertices, in particular to a method for updating the importance of dynamic flow graph data vertices based on random walks. Background technique [0002] The original PageRank concept refers to the ranking value of the importance of Web pages, and now it generally refers to the ranking value of the importance of vertices in a graph, which is usually obtained through continuous iterative convergence of the connection matrix and eigenvectors of the graph. In the era of big data, with the rapid development of social networks, many large-scale dynamic flow graphs have been produced. It is necessary to calculate the importance of each vertex in the graph, that is, PageRank, in order to carry out domain applications. For example, in a dynamic social network, it is necessary to instantly find a circle of friends or quickly discover criminal gangs based on the PageRank of t...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is exactly to provide a kind of dynamic stream graph data vertex importance update method based on random walk in order to overcome the defect that the PageRank calculation that exists in the above-mentioned prior art can not possess accuracy and real-time performance at the same time

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