Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning

Abstract

The author's academic behavior prediction aims to mine the behavioral relationships of authors from heterogeneous academic networks to promote scientific research cooperation and produce high-level and high-quality research results.Most of the existing methods of node representation learning do not consider the semantic feature,content feature,global structure of the node,etc.It is difficult to effectively learn the low-dimensional characteristics of the node in the network.In order to effectively integrate the multi-dimensional features and global structure of nodes,a heterogeneous network representation learning method(HNEMA) that integrates BiLSTM,attention mechanism and clustering algorithm is proposed to improve the predictive effect of author's academic behavior.HNEMA first integrates the multi-dimensional features of nodes based on BiLSTM and attention mechanism,aggregates the same type of neighbors on the same meta-path or different meta-paths,and then aggregates the multi-dimensional features of all neighbors of the node to be characterized.Based on this,a clustering algorithm is used to capture the global features of the node,so as to comprehensively and effectively learn the low-dimensional characteristics of the node.On the basis of comprehensive feature learning,logistic regression classifier is used to predict author's academic behavior.Validation experiments on three public datasets show that HNEMA has a certain degree of improvement in AUC and F1 indicators compared to other methods.

Keywords

• heterogeneous network|network representation learning|link prediction|meta-path