Network Embedding via Deep Prediction Model

• URL: http://arxiv.org/abs/2104.13323v1
• Date: Tue, 27 Apr 2021 16:56:00 GMT
• Title: Network Embedding via Deep Prediction Model
• Authors: Xin Sun, Zenghui Song, Yongbo Yu, Junyu Dong, Claudia Plant, and Christian Boehm
• Abstract summary: This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network.
• Score: 25.727377978617465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real-world networks can be reflected by dynamical transfer behaviors among nodes. This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. We first design a degree-weight biased random walk model to capture the transfer behaviors on the network. Then a deep network embedding method is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network, to utilize the 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 datasets including social networks, citation networks, biomedical network, collaboration network and language network. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization, classification, reconstruction and link prediction, and achieve promising performance compared with state-of-the-arts.

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