A Subgraph Similarity Query Method Based on Graph Measure

Abstract

The invention belongs to the technical field of databases, and in particular relates to a subgraph similarity query method based on graph measure. The subgraph similarity described in this scheme is: given the set D={G1,G2,...,Gn} of the data graph, the query graph q and the subgraph similarity distance threshold s, find out the similarity distance of all subgraphs with q from D Data plots smaller than s. This scheme first converts the estimation of the similarity distance of subgraphs of different measures into the estimation of graph measure distance, and then filters according to the estimation of graph measure distance, completes the subgraph similarity query, further constructs the graph measure tree, and performs subgraph based on the graph measure tree similar queries. Transform the estimation of the similarity distance of subgraphs of non-consensus measures into the estimation of graph measure distance, and then use the triangle inequality to filter; it supports similar queries of connected subgraphs and similar queries of disconnected subgraphs; design a graph The measure tree is used to index the data graphs in the data graph collection, which greatly improves the query efficiency.

Description

technical field [0001] The invention belongs to the technical field of databases, and in particular relates to a subgraph similarity query method based on graph measure. Background technique [0002] In recent years, graph-structured data has been widely used in many fields. Such as chemical informatics, bioinformatics, social network, intelligent transportation, computer vision, medical informatics, etc. Subgraph similarity queries are a very important type of query on graph data and have a wide range of applications. For example, on social networks, subgraph similarity query can be used to find people with certain relationships in the network; in chemical molecular databases, it is used to find molecules containing a specific structure; in protein interaction networks, it is used to find a group A protein that satisfies a specific functional connection; it is used in medicine for doctors' auxiliary diagnosis, etc. [0003] The existing subgraph similarity query methods ...

AI Technical Summary

Problems solved by technology

One is that it only supports similar queries under the definition of graph editing distance, and cannot support similar queries under other distance definitions

Second, only approximate solutions can be found

The subgraph similarity distance used by this method satisfies the triangle inequality, and the distance between the query graph q and a certain data graph G is estimated through the triangle inequality. If the estimated distance is definitely greater than t, then G is definitely not the answer. This method can only support connected sub-graphs Graph similarity query, does not support similarity query of non-connected subgraphs