Multi-dimensional graph network node clustering processing method, device and equipment

Abstract

The invention relates to a multi-dimensional graph network node clustering processing method, device and equipment, and the method comprises the steps of converting an original unweighted multi-dimensional graph network into a weighted multi-dimensional graph network according to the attribute similarity and structural similarity of nodes; according to the constructed intra-layer transition probability and cross-layer random walk transition probability, performing intra-layer and cross-layer multi-layer network random walk processing on the weighted multi-dimensional graph network to obtain a sampling sequence of each node of the weighted multi-dimensional graph network; converting the sampling sequence of each node into low-dimensional embedding based on a SkipGram model; performing clustering processing on the low-dimensional embedding of each node by adopting a K-means algorithm to obtain a clustering result of each node of the weighted multi-dimensional graph network; and embedding and projecting each low dimension into a two-dimensional space by adopting a dimension reduction technology, and displaying a clustering result by adopting a graph visualization technology. The clustering effect is obviously improved, and the clustering effect is better.

Description

technical field [0001] The present application relates to the technical field of network data processing, in particular to a multi-dimensional graph network node clustering processing method, device and equipment. Background technique [0002] Network theory can be used to model complex relationships between various entities in real life. Traditional methods for modeling the relationship between individuals in a system use a simple single network or a single-layer network, that is, the nodes in the network are of the same type And there is only one type of interaction network; among them, the nodes in the network represent the individuals in the complex system, and the edges represent the interaction relationship between the individuals. Multilayer networks can model the different interactions that exist between individuals. In other words, a multilayer network is a network that contains multiple layers, each of which is composed of an independent single-layer network (i.e.,...

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Problems solved by technology

However, in the process of realizing the present invention, the inventors found that the current node clustering technology of graph network has a technical problem of poor clustering effect

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