Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification
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 informatio...
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Veröffentlicht in: | Mathematical problems in engineering 2021, Vol.2021, p.1-9 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 1024-123X 1563-5147 |
DOI: | 10.1155/2021/6665588 |