基于自适应差异化图卷积的图注意力网络表示学习算法OA北大核心

In order to solve the limitation of traditional graph convolution network in dealing with complex relationships between nodes,a graph attention network representation learning algorithm based on adaptive differentiation graph convolution network is proposed.The differentiation graph convolution network is used to conduct differential sampling according to each node′s own characteristics and neighbor information,so as to capture the complex relationships between nodes.The two-stage key neighbor sampling method is used to mine important nodes first and retain randomness to complete the sampling of key neighbor nodes.In combination with graph attention network,the key neighbor node features are aggregated to their own nodes by means of local attention and adaptive learning weight distribution,so as to enhance the node feature representation.After training the network,the learning ability of network representation is enhanced further.The experimental results show that the proposed algorithm can optimize the degree of node aggregation and boundary clarity,and improve the accuracy and visualization of node classification.The algorithm also shows superior performance in network representation learning by paying attention to second-order neighbors and using double attention.