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.

中文翻译：

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CoANE：为属性网络嵌入建模上下文共现

属性网络嵌入（ANE）是学习低维向量，以便在嵌入空间中不仅可以保留网络结构，还可以保留节点属性。现有的 ANE 模型没有考虑图结构和属性之间的特定组合。虽然每个节点都有其结构特征，例如高度互连的邻居以及它们的某些属性分布模式，但每个节点的邻居不仅应该用多跳节点来描述，还应该考虑某些集群或社交圈。为了对这些信息进行建模，在本文中，我们提出了一种新的 ANE 模型，即上下文共现感知属性网络嵌入 (CoANE)。CoANE 的基本思想是对每个节点所涉及的不同模式的上下文属性进行建模，并应用卷积机制通过将每个属性视为一个通道来编码位置信息。上下文共现的学习可以捕捉到每个节点的潜在社交圈。为了更好地编码节点的结构和语义知识，我们设计了一个三向目标函数，包括正图似然、上下文负采样和属性重建。我们在链接预测、节点标签分类和节点聚类的任务中对五个真实数据集进行了实验。结果表明，CoANE 可以显着优于最先进的 ANE 模型。我们设计了一个三向目标函数，包括正图似然、上下文负采样和属性重建。我们在链接预测、节点标签分类和节点聚类的任务中对五个真实数据集进行了实验。结果表明，CoANE 可以显着优于最先进的 ANE 模型。我们设计了一个三向目标函数，包括正图似然、上下文负采样和属性重建。我们在链接预测、节点标签分类和节点聚类的任务中对五个真实数据集进行了实验。结果表明，CoANE 可以显着优于最先进的 ANE 模型。