基于不完全信息的深度网络表示学习方法

Deep Network Representation Learning Method on Incomplete Information Networks

网络表示学习的目标是将网络中的节点嵌入到低维的向量空间,为下游任务提供有效特征表示。在现实场景中,大规模网络通常具有不完整的链路,而现有的大多数网络表示学习模型都是在网络是完整的假设下设计的,因此其性能很容易受到链路缺失的影响。针对该问题,文中提出了一种基于不完全信息的深度网络表示学习方法DNRL(Deep Network Representation Learning)。首先采用转移概率矩阵将结构信息和属性信息进行动态融合,弥补了结构信息不完整带来的过大损失,然后采用一种具有强大特征提取能力的深度生成模型(变分自编码器)来学习节点的低维表示,并捕获网络数据中潜在的高非线性特征。在3个真实属性网络上的实验结果表明,与当前常用的网络表示学习模型相比,所提模型在不同程度链路缺失的节点分类任务中都明显地改善了分类效果,在可视化任务中更清晰地反映了节点的团簇关系。

The goal of network representation learning(NRL)is embedding network nodes into low-dimensional vector space,for effective feature representation of the downstream tasks.Due to the difficulty of information collection in the real-world scene-ries,large-scale networks often meet missing links between nodes.However,the most existing NRL models are designed on the foundation of complete information networks and that causes the poor robustness in incomplete networks.To solve this problem,a deep network representation learning(DNRL)method based on incomplete information networks is proposed.Firstly,a transfer probability matrix is used to dynamically mix the structural information and attribute information to cover the excessive loss caused by incomplete structural information.Then,a deep generative model variational autoencoder with powerful feature extraction capability is used to learn low-dimensional representation of nodes,and capture the potential high nonlinear features of nodes.Compared with the commonly used network representation learning methods,the experimental results on three real attri-bute networks show that the proposed model obviously improve effect in the node classification task with different degrees of link missing,visualization results clearly demonstrate the cluster relationship of nodes.