Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in the embedding space. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain clusters or social circles. To model such information, in this paper, we propose a novel ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE). The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel. The learning of context co-occurrence can capture the latent social circles of each node. To better encode structural and semantic knowledge of nodes, we devise a three-way objective function, consisting of positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets in the tasks of link prediction, node label classification, and node clustering. The results exhibit that CoANE can significantly outperform state-of-the-art ANE models.

翻译：属性嵌入网络( ANE) 是学习低维矢量, 这样不仅可以保存网络结构, 也可以保存嵌入空间中的节点属性。 现有的 ANE 模型并不考虑图形结构和属性之间的具体组合。 虽然每个节点都有其结构特征, 比如高度连接的邻居及其属性分布模式, 但每个节点的邻区不仅应该由多点节点描述, 并且考虑某些组合或社会圈 。 要建模这些信息, 我们在此文件中提出一个新的 ANE 模型, “ 环境、 环境、 环境、 意识、 网络嵌入( COANE ) 。 COANE 的基本理念是模拟每个节点涉及不同模式的背景属性的属性, 并应用进化机制将每个属性作为频道来编码定位信息。 学习环境共生关系可以捕捉到每个节点的潜在社会圈。 为了更好地将节点的结构性和语系知识编码, 我们设计一个三向目标函数, 包括正面的图表可能性、 背景负面取样、 和属性重组等五国级的模型 。 我们进行实际数据分类的实验 。