一种多结构及文本融合的网络表征方法

网络表征通过对网络结构的深度学习得到节点的矢量表征,挖掘网络中潜在的信息,是社会计算中的一种重要降维方法。针对一种融合了网络中的文本和结构的、基于矩阵分解的网络表征方法TADW,首先分析并讨论了文本属性矩阵在矩阵分解式中的位置对网络表征效果的影响,并对此方法进行了优化;在此基础上,提出了一种融合关系结构、交互结构和文本属性的社交网络表征方法。在多个数据集上的实验结果表明,该方法在多分类任务中优于其他经典网络表征方法。

Network Representation Model Based on Multi-architectures and Text Fusion

Network representation obtains the vector representations of nodes by deeply learning network structure,and mines the potential information on the network,which is an important method of reducing dimension in social computing.As for TADW,which is a network representation method based on matrix decomposition and combining text and structure,this paper first analyzed and discussed the influence of the location of text attributes matrix on network representation.Then,it proposed a social network representation method that incorporates relationship structure,interaction structure and textual attributes.Experimental results on multiple datasets show that the proposed method outperforms other classical network representation methods in classification tasks.