Similar directed subgraph search method and system based on subjective logic and feedforward neural network

Abstract

The invention belongs to the technical field of directed subgraph similarity judgment, and particularly relates to a similar directed subgraph searching method and system based on subjective logic and a feedforward neural network. A cost function does not need to be defined, and a similarity opinion of two few-node directed graphs (containing one node or two nodes) is given by utilizing a subjective logic theory based on an observation method; then, opinions of multiple experts are synthesized by applying a subjective logic increment desire operator, the scale of similar sub-graphs is gradually expanded by adopting a greedy algorithm and a subjective logic transfer operator, the objectivity and accuracy of similarity opinions are ensured, and the uncertainty of editing distance caused by different definitions of a cost function is solved; meanwhile, the time complexity of the algorithm cannot be quickly increased along with the increase of the number of nodes contained in the two graphs.

Description

technical field [0001] The invention belongs to the technical field of judging the similarity of directed subgraphs, and in particular relates to a similar directed subgraph search method and system based on subjective logic and feedforward neural network. Background technique [0002] In our life, there are a lot of data information that can be abstracted into graph structures, and most of them are based on directed graph structures. For example, in an urban transportation network, locations can be abstracted as nodes, and roads can be abstracted as directed edges, and the direction of the edge represents the direction of the road. With the deepening of research, people began to pay attention to the problem of graph similarity. The mainstream method to solve the graph similarity problem is based on the graph edit distance method. Take the EGED algorithm as an example, which measures the similarity of two graphs through a series of editing operations (deleting, inserting, ...

AI Technical Summary

Problems solved by technology

This method is feasible when the number of nodes is small, but as the number of nodes in the two graphs increases, the time complexity of the algorithm will increase exponentially, and it is not suitable for graphs with a large number of nodes.

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