PDF(Pubmed)
Abstract：
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node information by using edge information, thereby generating a new node feature. In updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node; the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the adaptive attention mechanism (AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution (MHGC). Finally, we adopt the cross-entropy (CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on the AAM, MHGC, and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). The experiments show that classification accuracy achieves outstanding performances on Cora, Citeseer, and Pubmed datasets.
摘要：
非欧几里得数据，例如社交网络和文档之间的引用关系，具有节点和结构信息。图卷积网络（GCN）可以自动学习节点特征和节点之间的关联信息。图卷积网络的核心思想是利用边缘信息聚合节点信息,从而生成新的节点特征。在更新节点特征时，有两个核心影响因素。一个是中心节点的相邻节点的数量;另一个是相邻节点对中心节点的贡献。由于以前的GCN方法没有同时考虑相邻节点对中心节点的数量和不同贡献，我们设计了自适应注意力机制(AAM)。为了进一步增强模型的表示能力，我们利用多头图卷积(MHGC)。最后,我们采用交叉熵（CE）损失函数来描述节点类别的预测结果与地面实况（GT）之间的差异。结合反向传播，这最终实现了节点的准确分类。基于AAM,MHGC,CE,我们设计了新颖的图形自适应注意力网络(GAAN)。实验表明，分类精度在Cora上取得了突出的性能，Citeseer,和发布的数据集。
