基于递归神经网络的集合顶点分类
Collective Vertex Classification Using Recursive Neural Network
摘要：
顶点的集体分类是基于顶点属性和链接结构为图中的每个顶点分配类别的任务。然而，由于邻近顶点的特征引入了噪声，一些现有的方法没有正确使用这些特征。在本文中，我们提出了一个基于图的递归神经网络框架的集体顶点分类。在这个框架中，我们通过递归神经网络从顶点的属性和相邻顶点的表示生成隐藏表示。在这个框架下，我们研究了两种类型的递归神经元，朴素递归神经元和长短期记忆单元。我们在四个真实世界的网络数据集上进行了实验。实验结果表明，我们的框架与长短期记忆模型取得了更好的结果，并优于几个竞争的基线方法。
英文摘要：
Collective classification of vertices is a task of assigning categories to each vertex in a graph based on both vertex attributes and link structure. Nevertheless, some existing approaches do not use the features of neighbouring vertices properly, due to the noise introduced by these features. In this paper, we propose a graph-based recursive neural network framework for collective vertex classification. In this framework, we generate hidden representations from both attributes of vertices and representations of neighbouring vertices via recursive neural networks. Under this framework, we explore two types of recursive neural units, naive recursive neural unit and long short-term memory unit. We have conducted experiments on four real-world network datasets. The experimental results show that our frame- work with long short-term memory model achieves better results and outperforms several competitive baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
