  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.
