基于AFP的有向加权注意力流网络链路预测

个性化推荐系统在减轻信息超载、提供个性化服务和辅助用户决策等方面应用广泛，链路预测是个性化推荐的重要方法之一。传统启发式链路预测方法仅考虑网络的图结构特征，缺乏对显式特征和隐式特征信息的应用，且大多数方法基于无向无权网络。针对传统链路预测方法存在的不足，基于集体注意力流网络和R-GCN方法，提出了链路预测算法AFP，将注意力流网络中2节点间不同的边方向抽象为2种边关系类型，并引入注意力机制学习网络中的节点属性和边属性，还综合考虑了网络的图结构特征、显式特征和隐式特征，最后通过评分函数得到三元组成立与否的概率，将链路预测问题转化为一个二分类问题，预测节点间的边属于某个关系类型的可能性。实验结果表明，相比于GCN、GAT等6个基准算法，该算法在准确度、精度和召回率等多个评价指标上均有提升。

AFP-based link prediction of directedweighted attention flow network

Personalized recommendation systems are widely used in reducing information overload, providing personalized services, and assisting users in decision-making. Link prediction is one of the important methods of personalized recommendation. Traditional heuristic link prediction methods only consider the graph structure characteristics of the network, and lack the application of explicit and implicit feature information, and most methods are based on undirected and unweighted networks. Aiming at the shortcomings of traditional link prediction methods, this paper proposes a link prediction method AFP based on the collective attention flow network and the R-GCN method. The different edge directions between the two nodes in the attention flow network are abstracted into two types of edge relations. The attention mechanism is introduced to learn the node attributes and edge attributes in the network, and the network's graph structure characteristics, implicit characteristics and explicit features are comprehensively considered. The scoring function is used to get the probability of the establishment of the triple, and the link prediction problem is transformed into a two-category problem, thus predicting the possibility that the edges between nodes belong to a certain type of relationship. Experiments show that, compared with 6 benchmark models such as GCN and GAT, this method improves the accuracy, precision, recall and other evaluation indicators.