Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding–attri2vec—that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.

中文翻译：

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通过子空间发现进行属性网络嵌入

网络嵌入旨在学习网络节点的潜在的低维向量表示，有效地支持各种网络分析任务。尽管关于网络嵌入的现有技术主要集中于保持网络拓扑结构以学习节点表示，但是最近提出的归因网络嵌入算法试图将丰富的节点内容信息与网络拓扑结构集成在一起，以提高网络嵌入的质量。实际上，网络经常具有稀疏的内容，不完整的节点属性以及节点属性特征空间和网络结构空间之间的差异，这严重恶化了现有方法的性能。在本文中，我们提出了一个统一的属性网络嵌入框架attri2vec，该框架通过在原始属性空间上进行的网络结构引导转换来发现潜在节点属性子空间，从而学习节点嵌入。所得的潜在子空间可以以更一致的方式尊重网络结构，以学习高质量的节点表示形式。我们制定了一个优化问题，该问题可以通过有效的随机梯度下降算法来解决，该算法的线性时间复杂度与节点数有关。我们研究了对节点属性执行的一系列线性和非线性变换，并通过经验验证了它们在各种类型的网络上的有效性。attri2vec的另一个优势是它能够解决样本外问题，通过学习的映射功能，可以从节点属性中推断出新出现的节点的嵌入。在各种类型的网络上进行的实验证实，对于节点分类，节点聚类以及样本外链接预测任务，attri2vec优于最新的基准。本文的源代码位于https://github.com/daokunzhang/attri2vec。