DeepAI AI Chat
Log In Sign Up

Network Embedding via Deep Prediction Model

by   Xin Sun, et al.

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.


page 1

page 14


Community Aware Random Walk for Network Embedding

Social network analysis provides meaningful information about behavior o...

Temporal Network Representation Learning via Historical Neighborhoods Aggregation

Network embedding is an effective method to learn low-dimensional repres...

node2vec: Scalable Feature Learning for Networks

Prediction tasks over nodes and edges in networks require careful effort...

BiasedWalk: Biased Sampling for Representation Learning on Graphs

Network embedding algorithms are able to learn latent feature representa...

Network Embedding: An Overview

Networks are one of the most powerful structures for modeling problems i...

Global and Local Feature Learning for Ego-Network Analysis

In an ego-network, an individual (ego) organizes its friends (alters) in...

Boost CTR Prediction for New Advertisements via Modeling Visual Content

Existing advertisements click-through rate (CTR) prediction models are m...