图表示学习是实现各类图挖掘任务的基础.现实中的图数据不仅包含复杂的网络结构，还包括多样化的节点信息.如何将网络结构和节点信息更加有效地融入图的表示学习中，是一个重要的问题.为了解决这一问题，基于深度学习，提出了融合节点先验信息的图表示学习方法.该方法将节点特征作为先验知识，要求学习到的表示向量同时保持图数据中的网络结构相似性和节点特征相似性.该方法的时间复杂度为O（|V|），其中，|V|为图节点数量，表明该方法适用于大规模图数据分析.同时，在多个数据集上的实验结果表明：所提出的方法相比目前流行的几种基线方法，在分类任务上能够获得良好而稳定的优势.

Graph embedding is a fundamental technique for graph data mining. The real-world graphs not only consist of complex network structures, but also contain diverse vertex information. How to integrate the network structure and vertex information into the graph embedding procedure is a big challenge. To deal with this challenge, a graph embedding method, which is based on deep leaning technique while taking into account the prior knowledge on vertices information, is proposed in this paper. The basic idea of the proposed method is to regard the vertex features as the prior knowledge, and learn the representation vector through optimizing an objective function that simultaneously keeps the similarity of network structure and vertex features. The time complexity of the proposed method is O(|V|), where|V|is the count of vertices in the graph. This indicates the proposed method is suitable for large-scale graph analysis. Experiments on several data sets demonstrate that, compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results for the task of node classification.

TP18

NSFC-广东联合基金（U1501254）；国家自然科学基金（61472089，61572143，61502108）