融合连边符号语义信息的网络表示学习算法

Network representation learning algorithm incorporated with edge signed semantic information

为融合连边符号语义信息提升网络表示学习质量,针对现有算法处理复杂连边符号语义信息能力较弱问题,提出一种融合连边符号语义信息的网络表示学习算法,将包含正负关系的连边符号语义信息引入网络表示学习过程。首先,该算法设计基于三层感知机的关系预测模型刻画节点间不同类型的上下文链接关系;然后,引入随机游走策略实现上下文链接采样以适应大规模网络场景训练需求。在三个数据集中实验表明,该算法能够有效建模节点间不同类型的上下文链接关系,挖掘其中包含的复杂语义信息,相比目前最优的SIDE方法,所提算法的性能分别提高了0.31%、1.3%和1.85%。

In order to enhance the network representation learning quality with the edge signed semantic information,focusing on the weakness of existing fusion methods in dealing with complex edge signed semantic information,this paper proposed a network representation learning algorithm incorporating with edge signed semantic information,and introduced the edge signed semantic information containing positive and negative relations into the network representation learning process.Firstly,this paper designed a relationship prediction model based on the three-layer perceptron to depict different types of context link relations between nodes.Then it introduced the random walk strategy to implement context link sampling to adapt the large-scale network scenarios.Experiments on three data sets show that this algorithm can effectively model different types of context links between nodes and mine the complex semantic information contained in them.Compared with the current optimal SIDE method,the performance of the proposed algorithm is improved by 0.31%,1.3%and 1.85%.