A Classification Method for Academic Resources Based on a Graph Attention Network

Jie Yu, Yaliu Li, Chenle Pan and Junwei Wang
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Jie Yu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yaliu Li: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Chenle Pan: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Junwei Wang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2021, vol. 13, issue 3, 1-16

Abstract: Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods.

Keywords: academic resource; attention; association features; content attributes; classification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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