随在线社交网络规模的不断增长,传统的链路预测方法难以捕获每个用户的全面特征信息.针对该问题,提出一种基于图注意力网络的链路预测方法(link prediction based on graph attention network,LP-GAT).首先,将在线社交网络表示为图结构数据,反映完整的用户属性信息和社交网络结构信息;其次,在图神经网络模型中引入注意力机制,更准确地刻画用户的社交特征信息,并生成用户节点的嵌入表示;最后,将节点嵌入表示输入分类器进行模型性能评估.在4个真实的在线社交网络数据集进行链路预测实验,结果表明所提模型较传统链路预测方法的性能更优.
Link prediction for online social networks via graph attention network
With the increasing scale of online social networks,traditional link prediction methods are difficult to capture comprehensive feature information of each user.To address this problem,a no-vel link prediction method for online social networks based on graph attention network(LP-GAT)is proposed in this paper.First,the online social network is represented as a graph to preserve the complete user attribute information and social network structural information.Secondly,the atten-tion mechanism is introduced into the graph neural network model to more accurately depict the user's social feature information and generate the embedded representation of the user node.Final-ly,the performance of the model is evaluated by feeding the node embedding representations into a classifier.The results of link prediction experiments on 4 real online social network data sets show that the proposed model has better performance than traditional link prediction methods.
online social networkgraph neural networksgraph attention networklink prediction
万方数据
维普
