Open AccessA Study on Hybrid Hierarchical Network Representation Learning
Author(s) -
Yongxiang Hu
Publication year - 2021
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.20611
Subject(s) - computer science , representation (politics) , initialization , theoretical computer science , embedding , vector space , graph , network structure , artificial intelligence , mathematics , geometry , politics , political science , law , programming language
Network representation learning (NRL) aims to convert nodes of a network into vector forms in Euclidean space. The information of a network is needed to be preserved as much as possible when NRL converts nodes into vector representation. A hybrid approach proposed in this paper is a framework to improve other NRL methods by considering the structure of densely connected nodes (community-like structure). HARP [1] is to contract a network into a series of contracted networks and embed them from the high-level contracted network to the low-level one. The vector representation (or embedding) for a high-level contracted network is used to initialize the learning process of a low-level contracted graph hierarchically. In this method (Hybrid Approach), HARP is revised by using a well-designed initialization process on the most high-level contracted network to preserve more community-like structure information.