摘要
链路预测作为复杂网络分析的一个重要分支,在不同领域中有着广泛的应用,而且通过进一步提取网络结构信息可以提高链路预测的精度。提出了一种基于结构深度网络嵌入和关联相似性的链路预测算法(Structural Deep Correlation Similarity Network Embedding,SDCSNE)。SDCSNE算法结合了网络嵌入捕捉高维非线性网络结构的特征,将网络映射到向量空间中,这些映射向量的内积即为对应节点的相似性,并保持了全局和局部的网络结构,获得了更加稳定的网络结构信息;SDCSNE算法还融入了节点的关联性,以提高预测的准确性。实际结果表明,在链路预测任务中,SDCSNE算法具有良好的性能。
Link prediction,as an important branch of complex network analysis,has been widely used in different fields.A link prediction algorithm(SDCSNE)based on structural depth network embedding and association similarity is proposed.The algorithm combines the characteristics of network embedding to capture the high-dimensional nonlinear network structure,and maps the network to the vector space.The inner product of these mapping vectors is the similarity of the corresponding nodes,and maintains the global and local network structure,so as to obtain more stable network structure information.The SDCSNE algorithm also incorporates the relevance of nodes to improve the accuracy of prediction.The practical results show that SDCSNE algorithm has good performance in link prediction.
作者
冯仁君
陈海雁
王芳
FENG Ren-jun;CHEN Hai-yan;WANG Fang(Suzhou Power Supply Branch,State Grid Jiangsu Electric Power Limited Company,Suzhou,Jiangsu 215004,China)
出处
《计算技术与自动化》
2020年第2期114-118,共5页
Computing Technology and Automation
基金
国家自然科学基金资助项目(61303108)。
关键词
链路预测
复杂网络
相似性
网络嵌入
关联
link prediction
complex networks
similarity
network embedding
correlation
