Federal graph clustering method and device based on distributed graph embedding and readable storage medium

Abstract

The invention provides a federated graph clustering method and device based on distributed graph embedding and a read storage medium. The method comprises the steps that a first graph is constructed based on first-party data, and a second graph is constructed based on second-party data; performing encryption intersection on the first-party data and the second-party data, determining common nodes in the first graph and the second graph, and associating the first graph and the second graph according to the common nodes to obtain a federated graph; learning the federated graph by using a distributed graph embedding algorithm based on random walk, and determining a first graph embedding vector [PiA, PiB] starting from the first graph and a second graph embedding vector [PiA ', PiB'] starting from the second graph; and performing clustering analysis on the first graph embedding vector [PiA, PiB] and the second graph embedding vector [PiA ', PiB'] of the federated graph on the basis of a federated clustering method to obtain a clustering result. By utilizing the method, federated graph clustering can be performed on privacy data of both parties, and a better clustering effect is obtained.

Description

technical field [0001] The invention belongs to the field of clustering, and in particular relates to a federated graph clustering method, device and readable storage medium based on distributed graph embedding. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] The current federated learning technology has high application potential for the joint use of data that does not leave the database, and to mine the value of multi-party data. However, the main supported algorithms are traditional machine learning classification models, regression models, etc., focusing on the evaluation of individual value portraits. The mining of potential gang behavior is relatively lacking. At the same time, because graph computing involves multiple rounds of topological interactive computing of...

AI Technical Summary

Problems solved by technology

[0003] The current federated learning technology has high application potential for the joint use of data that does not leave the database, and to mine the value of multi-party data. However, the main supported algorithms are traditional machine learning classification models, regression models, etc., focusing on the evaluation of individual value portraits. The mining of potential gang behavior is relatively lacking. At the same time, because graph computing involves multiple rounds of topological interactive computing of multi-party data, the research on the development of graph mining algorithms based on privacy computing is relatively weak, and there are few achievements in the industry.

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