摘要
为提高多重异质网络中节点重要度评估的准确性,提出一种基于图嵌入的节点重要度评估方法。通过随机游走采样邻居节点,聚合节点在同种连边类型和不同连边类型下的节点特征,利用多层感知机将特征映射到嵌入空间,得到嵌入向量;根据节点的嵌入向量和局部结构特征构建重要度评估指标。在CElegans和CS-Aarhus等数据集上的实验结果表明,与多重介数中心性、有偏页面排序和多重证据中心性等方法相比,所提方法具有更高的准确性。
To improve the accuracy of node importance evaluation in multiplex heterogeneous network(MHEN), a method of node importance evaluation is proposed for MHEN based on graph embedding. For the same type and different types of edges, the features of the nodes are aggregated after random walk sampling neighbor nodes, and the features are mapped to the embedding space by multi-layer perceptron to obtain the embedding vectors. Then, the node importance evaluation index for MHEN is constructed by the embedding vectors of nodes and features of local structure. The experimental results on mainstream datasets, such as CElegans and CS-Aarhus show that compared with multiplex betweenness centrality, biased PageRank and multiplex evidential centrality, the proposed method performs better in term of the accuracy.
作者
舒坚
尧小龙
李睿瑞
SHU Jian;YAO Xiaolong;LI Ruirui(School of Software,Nanchang Hangkong University,Nanchang 330063,China)
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2022年第4期104-109,共6页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(62062050,61962037)。
关键词
多重异质网络
节点重要度评估
图嵌入
multiplex heterogeneous network
node importance evaluation
graph embedding
