Learning Network Representation Through Reinforcement Learning

Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn r...

Full description

Saved in:
Bibliographic Details
Main Authors: Shen, Siqi, Fu, Yongquan, Jia, Adele Lu, Su, Huayou, Wang, Qinglin, Wang, Chengsong, Dou, Yong
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Navigation
Network topology
Reinforcement learning
Signal processing
Speech processing
Task analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn representations from it. In contrast, we propose, reinforcement learning network representations (RLNet), which explores the idea of using reinforcement learning to learn to explore the network and to obtain network representations. Based on reward signals, RLNet learns an actor which uses a policy to determine the network navigation actions. RLNet uses node representations to parameterize its policy, and the representations are learned together with the policy. Through experiments based on multiple datasets, we show that RLNet can obtain promising results in link prediction tasks.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9053879