融合二连通模体结构信息的节点分类算法

Node classification algorithm fusing 2-connected motif-structure information

节点表示学习将图结构数据信息编码到低维的潜在空间中,在节点分类、聚类、链路预测等机器学习任务中被广泛应用。在复杂网络中,节点与节点之间不仅存在直接相连的低阶结构,也存在以特殊连接模式形成的高阶结构,称为模体。提出一种融合二连通模体结构信息的节点分类算法(FMI),利用节点间高阶二连通模体信息学习节点表示,完成节点分类任务。首先,统计网络中的二连通模体,利用其中信息提出一个节点重要性的度量指标——模体比值。根据模体比值计算采样概率进行邻域采样;构造一个带权辅助图以融合网络节点连接的低阶关系与高阶关系,对节点进行加权邻域聚合以得到节点表示。在5个数据集Cora、Citeseer、Pubmed、Wiki和DBLP上执行节点分类任务,与5种经典基准算法进行对比,所提算法FMI在准确度和F1-分数等指标上表现良好。

Node representation learning has been widely applied in machine learning tasks,such as node classification,clustering and link prediction,since it can encode graph structure data information into low-dimensional potential space.In complex networks,nodes are interacted through not only low-order interactions,but also higher-order interactions formed by special connection modes.The higher-order interactions of a complex network are also called motifs.A node classification algorithm Fusing 2-connected Motif-structure Information(FMI)was proposed to use motif information among nodes to obtain node representation for node classification tasks.Firstly,the 2-connected motifs in the network were counted.A measure index of node importance,named motif-ratio,was proposed by using the motif information in the node;and a sampling probability was calculated according to the motif-ratio to carry out neighborhood sampling.A weighted auxiliary graph was constructed to fuse the low-order relations and the high-order relations of network nodes to aggregate neighborhoods weightedly.The node classification was performed on 5 datasets,Cora,Citeseer,Pubmed,Wiki and DBLP.By comparing with 5 classical baseline algorithms,the proposed algorithm FMI shows better performance on Accuracy,F1-score and other indicators.