计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 38-41.doi: 10.11896/j.issn.1002-137X.2018.07.006
李佳艺1,赵宇1,王莉2
LI Jia-yi1,ZHAO Yu1,WANG Li2
摘要: 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。
中图分类号:
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