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