计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 119-124.doi: 10.11896/jsjkx.190900027
张虎, 周晶晶, 高海慧, 王鑫
ZHANG Hu, ZHOU Jing-jing, GAO Hai-hui, WANG Xin
摘要: 随着神经网络技术的快速发展面向复杂网络数据的网络表示学习方法受到越来越多的关注其旨在学习网络中节点的低维度潜在表示并将学习到的特征表示有效应用于基于图的各种分析任务.典型的浅层随机游走网络表示学习模型主要基于节点结构相似和节点内容相似不能同时有效捕获节点结构和内容的相似信息因此在结构和内容等价混合的网络数据上表现较差.为此探索了节点结构相似和节点内容相似的融合特征提出了一种基于无监督浅层神经网络联合学习的表示方法SN2vec.实验分别利用节点结构和内容等价混合的Brazilianair-trafficAmericanair-trafficWikipedia数据集在多标签分类和降维可视化任务上进行验证.结果显示SN2vec在多标签分类任务中的Micro-F1值优于现有的浅层随机游走网络表示方法并且可以较好地学习到潜在结构表示一致的节点.
中图分类号:
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