Deep Network Representation Learning Method on Incomplete Information Networks

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.

Abstract licence: CC BY-NC-ND

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