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

A feed-forward neural network and directed subgraph technology, applied in the field of similar directed subgraph search methods and systems, can solve problems such as increasing time complexity and not applicable to graphs with a large number of nodes

Inactive Publication Date: 2021-09-14
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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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 tim

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

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[0057] In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work are protected by the present invention. scope.

[0058] Observation is the basic method for humans to discriminate the similarity of two figures. For graphs with fewer nodes, one can easily judge the similarity of two graphs by observation. For graphs with a large number of nodes, human observation is obviously not feasible, and it is difficult to find all similar subgraphs in the two graphs....

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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, ...

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Application Information

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IPC IPC(8): G06F16/901G06F16/903G06N3/04
CPCG06F16/9024G06F16/903G06N3/044
Inventor 张玉臣黄金明周洪伟汪永伟孙怡峰胡浩
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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