Hierarchical Triplet Attention Pooling for Graph Classification

Recent years, graph neural network have been introduced to handle the structural data in non-Euclidean space. Graph neural network learns the representation of the whole network through the representation of nodes. The pooling layer plays an important role in gradually reducing the network to a sufficient small coarse graph. This work will propose an end-toend pooling method, which builds pseudo-edge weights to form a subset of important features. It updates the coarse-grained node embedding by focusing on the common information at different locations, to improve the attention to the valuable information. Moreover, we propose a transfer operator that integrates into the convolutional layer, which not only integrates the intrinsic features of the node but also learns rich community properties. Experimental results show that our proposed pooling method can be combined with multiple convolutional layers to achieve optimal results in graph classification tasks. In addition, our proposed model method is generally superior to most baseline graph classification methods.