Complex network link prediction method and system based on logical reasoning and graph convolution

Abstract

The invention discloses a complex network link prediction method and system based on logical reasoning and graph convolution. The method comprises the following steps: constructing a knowledge graph corresponding to a complex network, and obtaining a training set; performing relation reasoning on each entity pair in the training set through a first-order logical reasoning network with default, and obtaining a relation confidence coefficient matrix through mapping; based on the relation confidence coefficient matrix, performing iterative training on a graph convolutional neural network based on iterative attention through a centralized training decentralized execution mechanism and a local relation attention mechanism to obtain first probability distribution; calculating second probability distribution according to a relation weight matrix and a relation confidence coefficient matrix output by network iteration; obtaining a Wasserstein distance between the first probability distribution and the second probability distribution according to a joint evaluation function; iteratively updating the two networks according to a Wasserstein distance to obtain a link prediction model; and complementing the knowledge graph according to the link prediction model. The link prediction efficiency is high.

Description

technical field [0001] The invention belongs to the technical field of complex network analysis, and in particular relates to a complex network link prediction method and system based on logical reasoning and graph convolution. Background technique [0002] A complex network is an abstraction of real networks such as social networks, citation networks, biological metabolism networks, and cooperative relationship networks. Most of the problems in knowledge graphs can be expressed in the form of networks. To build a complete complex network, it can be combined with knowledge graphs. This is accomplished through link prediction. The link prediction task is to predict the missing edges in the complex network, or predict the edges that may appear in the future, and the knowledge graph stores the complex relationship between entities and entities, including a large number of fact triples composed of entities and the relationship between entities, but In a large-scale knowledge gr...

AI Technical Summary

Problems solved by technology

The link prediction task is to predict the missing edges in the complex network, or predict the edges that may appear in the future, and the knowledge graph stores the complex relationship between entities and entities, including a large number of fact triples composed of entities and the relationship between entities, but In large-scale knowledge graphs, due to the sparsity of data, knowledge is incomplete, and there are many hidden knowledge that have not been mined, so link prediction tasks are required

The existing complex network link prediction methods often use R-GCN (Relational Graph Convolutional Network, relational graph convolutional network), but the link prediction performance of R-GCN is low

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