Year: 2019
Type: conference-paper
Abstract: Network embedding has attracted increasing attention in recent few years, which is to learn a low-dimensional representation for each node of a network to benefit downstream tasks, such as node classification, link prediction, and network visualization. Essentially, the task of network embedding can be decoupled into discovering the proximity in the original space and preserving it in the low dimensional space. Only with the well-discovered proximity can we preserve it in the low-dimensional space. Thus, it is critical to discover the proximity between different nodes to learn good node representations. To address this problem, in this paper, we propose a novel proximity generative adversarial network (ProGAN) which can generate proximities. As a result, the generated proximity can help to discover the complicated underlying proximity to benefit network embedding. To generate proximities, we design a novel neural network architecture to fulfill it. In particular, the generation of proximities is instantiated to the generation of triplets of nodes, which encodes the similarity relationship between different nodes. In this way, the proposed ProGAN can generate proximities successfully to benefit network embedding. At last, extensive experimental results have verified the effectiveness of ProGAN.
Authors: Hongchang Gao, Jian Pei, Heng Huang
Institutions: University of Pittsburgh, Simon Fraser University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: SCH: EXP: Collaborative Research: Privacy-Preserving Framework for Publishing Electronic Healthcare Records, BIGDATA: Collaborative Research: IA: Big Imaging-Omics Data Mining Framework for Precision Medicine, SCH: INT: New Machine Learning Framework to Conduct Anesthesia Risk Stratification and Decision Support for Precision Health, III: Medium: Collaborative Research: Robust Large-Scale Electronic Medical Record Data Mining Framework to Conduct Risk Stratification for Personalized Intervention, III: Small: Robust Large-Scale Data Mining for Knowledge Discovery in Depression Thought Records, BIGDATA: IA: Collaborative Research: Asynchronous Distributed Machine Learning Framework for Multi-Site Collaborative Brain Big Data Mining, ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression
OpenAlex ID: W2950352474