窦伟;张维玉

• 1:齐鲁工业大学(山东省科学院)计算机科学与技术学院，济南 250353

摘要(Abstract)：

网络嵌入对网络数据进行有效表示，应用于后续的机器学习任务。现有的基于随机游走的网络嵌入算法主要是针对网络的拓扑信息来进行表示学习，忽略了网络中节点的属性信息。但是网络中节点的属性信息可以为网络嵌入提供很好的信息来源。基于上述考虑提出一种基于偏置随机游走的属性网络嵌入方法ANEBRW（Attributed Network Embedding of Biased Random Walk），通过进行偏置随机游走采样兼顾网络表示的局部性和全局性，结合节点属性信息得到更好的网络表示。在真实的属性网络上的实验结果表明，本文所提方法在节点分类任务中准确率得到了一定的提升。

关键词(KeyWords)： 网络嵌入;随机游走;属性网络;机器学习

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（61502259）、国家重点研发计划（2018YFC704）、山东省自然科学基金（ZR2017MF056）

作者(Author): 窦伟;张维玉

Email:

DOI: 10.16442/j.cnki.qlgydxxb.2019.05.011

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