Social network graph abstract generation method based on incremental computation

Abstract

The invention discloses a social network graph abstract generation method based on incremental computation, and belongs to the field of social networks. The method comprises the following steps: carrying out tensor representation on a social network graph in a target time period to obtain a Boolean tensor TG; carrying out tensor decomposition on the Boolean tensor TG to obtain decomposed node matrixes N1 and N2, attribute matrixes A1,..., A<h-3> and a time matrix T; clustering the node matrix N1 or N2 to obtain a clustering cluster center and the type to which each node belongs; regarding thecluster center as hyper-points of the graph abstract, calculating hyper-edge weights between the hyper-points to obtain the graph abstract. According to the method, multi-dimensional data fusion is carried out on nodes, node attributes and timestamps of the social network, and unified expression of high-dimensional graph data and Boolean tensor expression of a complex social network are realized based on binaryzation of a social network graph and high-dimensional expression characteristics of tensors. Incremental CP decomposition is introduced, prior information such as the decomposition result of the old graph tensor is fully utilized, the size of the decomposition tensor is reduced, and the decomposition efficiency of the graph abstract is improved.

Description

technical field [0001] The invention belongs to the field of social networks, and more specifically relates to a method for generating abstracts of social network graphs based on incremental calculations. Background technique [0002] Social network analysis is a hot topic in the data mining community in recent years, querying and reasoning about the interrelationships between entities in a social network can reveal interesting and deep insights into various phenomena. However, due to the dynamic and changeable structure of social networks, complex data and other characteristics, the expression and mining of social network graph data are limited by computing resources and cost overhead. Therefore, the starting point for analyzing these complex large graph data is usually a concise representation, the graph summary. It helps to understand these datasets and to represent queries in a meaningful way. Graph summarization plays a very important role in the processing of graph d...

AI Technical Summary

Problems solved by technology

In addition, for time-series dynamic graphs, the current method will double-calculate historical data, resulting in low computational efficiency

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