Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification

Fangyuan Lei, Xun Liu, Zhengming Li, Qingyun Dai and Senhong Wang

Mathematical Problems in Engineering, 2021, vol. 2021, 1-9

Abstract:

Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k -hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6665588

DOI: 10.1155/2021/6665588

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