基于子空间发现的属性网络嵌入
Attributed Network Embedding via Subspace Discovery
摘要：
网络嵌入旨在学习网络节点的潜在低维向量表示，有效地支持各种网络分析任务。虽然关于网络嵌入的现有技术主要集中在保留网络拓扑结构以学习节点表示，但是最近提出的属性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相结合，以提高网络嵌入的质量。现实中，网络往往存在内容稀疏、节点属性不完整以及节点属性特征空间与网络结构空间不一致等问题，严重影响了现有方法的性能。在本文中，我们提出了一个统一的框架属性网络嵌入-attri 2 vec-学习节点嵌入通过发现一个潜在的节点属性子空间，通过网络结构引导的变换进行原始属性空间。由此产生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。我们制定了一个优化问题，解决了一个有效的随机梯度下降算法，线性时间复杂度的节点数。我们研究了一系列的线性和非线性变换节点属性和经验验证其有效性的各种类型的网络。attri 2 vec的另一个优点是它能够解决样本外问题，其中新节点的嵌入可以通过学习的映射函数从它们的节点属性中推断出来。在各种类型的网络上进行的实验证实，attri 2 vec优于节点分类、节点聚类以及样本外链接预测任务的最新基线。本文的源代码可在https://github.com/daokunzhang/attri2vec上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
