Network Embedding via Deep Prediction Model

TL;DR

This paper introduces a deep prediction model-based network embedding framework that captures transfer behaviors in networks, improving feature representation for various tasks like clustering, classification, and link prediction.

Contribution

It proposes a novel network embedding method combining biased random walks, deep prediction models, and Laplacian space optimization to better reflect network transfer behaviors.

Findings

Effective in multiple network types including social and biomedical networks

Improves performance in clustering, visualization, and link prediction tasks

Outperforms state-of-the-art methods in experiments

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.