Graph query method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to a graph query method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of graph query, the method comprises the stepsof performing graph embedding mapping on a first graph to a vector space to obtain a first vector space; performing graph embedding mapping on the second graph to the vector space to obtain a secondvector space; calculating and clustering the second vector space to obtain a node clustering cluster of the second graph; based on the first vector space and the node clustering cluster, calculating acandidate node set similar to the node of the second graph in the nodes of the first graph; calculating the maximum connected subgraph of each node in the candidate node set to obtain a plurality ofconnected subgraphs; and obtaining graphs with similar topological structures of the second graph in the first graph from the plurality of connected sub-graphs, and taking the graphs as query resultsof the second graph in the first graph. According to the embodiment of the invention, the graph query efficiency is effectively improved, and the generalization ability of the query result is improved.

Description

Technical field [0001] This application relates to the technical field of graph query, and in particular to a graph query method, device, electronic equipment, and computer-readable storage medium. Background technique [0002] Generally, in many real-world applications such as social networks, semantic webs, transaction networks, etc., graph databases have been widely used as important tools for modeling and querying complex graph data. A graph is a data structure in which nodes can have zero or more adjacent elements. At present, many kinds of queries about graphs are widely studied, among which subgraph matching is a basic type of graph query. A typical subgraph matching query, given a query graph and a large graph, will retrieve those subgraphs of the query graph that exactly match the graph topology in the large graph. [0003] In the prior art, the Ullmann algorithm and the vf2 algorithm are classic subgraph matching methods. Neo4j is a commonly used graph database system....

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Problems solved by technology

[0005] The purpose of this application is to provide a graph query solution, and then at least to a certain extent solve the problem that the graph query methods in the prior art are slow in actual calculation, the time overhead of the algorithm is large, and the query results are not universal. problems with low generalization ability, thereby effectively improving the efficiency of graph query and improving the generalization ability of query results

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