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Structural attention network for graph
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DOI：10.1007/s10489-021-02214-8.png)
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Abstract
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We present a structural attention network (SAN) for graph modeling, which is a novel approach to learn node representations based on graph attention networks (GATs), with the introduction of two improvements specially designed for graph-structured data. The transition matrix was used to differentiate the structures between the nodes. The output features of nodes in the graph are represented as the concatenation of multi-order features to differentiate the structures among multiple orders. This novel neural network is based on a graph attention network, which makes the model pay attention to the topology of the graph. Using various experiments on citation networks and a protein-protein interaction dataset, we demonstrate the benefits of structural information in graph attention mechanisms.
Keywords:
Attention mechanism
Structural attention
Graph network
Graph learning
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