Network Structure and Transfer Behaviors Embedding via Deep Prediction Model

Author(s)
Xin Sun, Zenghui Song, Junyu Dong, Yongbo Yu, Claudia Plant, Christian Böhm
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.

Organisation(s)
Research Group Data Mining and Machine Learning, Research Network Data Science
Pages
5041-5048
No. of pages
8
DOI
https://doi.org/10.1609/aaai.v33i01.33015041
Publication date
2019
Peer reviewed
Yes
Austrian Fields of Science 2012
102033 Data mining
ASJC Scopus subject areas
Artificial Intelligence
Portal url
https://ucrisportal.univie.ac.at/en/publications/afbe2f8c-0cd3-40bf-a2e0-be6573246536