Academic cooperation relation prediction method based on heterogeneous graph neural network

Abstract

The invention discloses an academic cooperation relationship prediction method based on a heterogeneous graph neural network, and the method comprises the steps: collecting the work information of an author to construct a work data set and an academic heterogeneous graph, and the nodes in the graph comprise author nodes, work nodes, affiliation nodes and content nodes; using a DeepWalk algorithm and a Text-CNN (Convolutional Neural Network) model to extract information in the learning heterogeneous graph to obtain an embedding vector of an author node; aggregating meta-paths in the learning heterogeneous graph based on author nodes by using a multi-head attention mechanism, and obtaining a long-term interest embedding representation of each author node; obtaining a short-term interest embedding representation of the author based on LSTM and an attention mechanism; and constructing a binary classification model by using a binary classification prediction method, and inputting the work data set into the binary classification model for training to obtain an academic cooperation prediction model. According to the method, the potential cooperation interest of the author in a certain time can be captured, a better recommendation effect is obtained, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and in particular relates to a method for predicting an academic cooperation relationship based on a heterogeneous graph neural network. Background technique [0002] In recent years, with the rapid development of science and technology, the trend of scientific research tasks and innovations has changed from relying on individual achievements to cooperation. More than 90% of innovation in this century comes from collaboration. At the same time, the development of science and technology has also brought about the diversification and complexity of research problems, and the cross-integration of multiple disciplines is becoming more and more common. As a result, cooperation is no longer as sparse and single as before, but becomes more and more complex and diverse. The changing research directions of scholars and the increasing number of cooperative relationships make finding the most...

AI Technical Summary

Problems solved by technology

However, most of the existing heterogeneous graph neural network algorithms are general-purpose models, focusing on the characteristics of the heterogeneous graph itself, and lack of attention to the subdivision of academic cooperation prediction problems in heterogeneous academic networks.

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