Network Structure and Transfer Behaviors Embedding via Deep Prediction Model

  • Xin Sun Ocean University of China
  • Zenghui Song Ocean University of China
  • Junyu Dong Ocean University of China
  • Yongbo Yu Ocean University of China
  • Claudia Plant University of Vienna
  • Christian Böhm Ludwig Maximilian University of Munich

Abstract

Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning