Sequential attention layer-wise fusion network for multi-view classification

doi:10.1007/s13042-024-02260-x

2024-07-01

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DOI：10.1007/s13042-024-02260-x

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摘要

En 中文

Graph convolutional network has shown excellent performance in multi-view classification. Currently, to output a fused node embedding representation in multi-view scenarios, existing researches tend to ensure the consistency of embedded node information among multiple views. However, they pay much attention to the immediate neighbors information rather than multi-order node information which can capture complex relationships and structures to enhance feature propagation. Furthermore, the embedded node information in each convolutional layer has not been fully utilized because the consistency is frequently achieved by the final convolutional layer. To tackle these limitations, we develop a new end-to-end multi-view learning architecture: sequential attention Layer-wise Fusion Network for multi-view classification (SLFNet). Motivated by the fact that for each view, multi-order node information is hidden in the multiple layer-wise node embedding representations, a set of sequential attentions can then be calculated over those multiple layers, which provides a novel fusion strategy from the perspectives of multi-order. The contributions of our architecture are: (1) capturing multi-order node information instead of using the immediate neighbors, thereby obtaining more accurate node embedding representations; (2) designing a sequential attention module that allows adaptive learning of node embedding representation for each layer, thereby attentively fusing these layer-wise node embedding representations. Our experiments, focusing on semi-supervised node classification tasks, highlight the superiorities of SLFNet compared to state-of-the-art approaches. Reports on deeper layer convolutional results further confirm its effectiveness in addressing over-smoothing problem.

Keyword:

Attention mechanism

Graph convolutional networks

Multi-view

Semi-supervised learning