Open AccessNetwork Representation Learning Algorithm Based on Community Folding
Author(s) -
Dongming Chen Dongming Chen,
Mingshuo Nie Dongming Chen,
Jiarui Yan Mingshuo Nie,
Jiangnan Meng Jiarui Yan,
Dongqi Wang Jiangnan Meng
Publication year - 2022
Publication title -
wǎngjì wǎnglù jìshù xuékān
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.231
H-Index - 22
eISSN - 2079-4029
pISSN - 1607-9264
DOI - 10.53106/160792642022032302020
Subject(s) - computer science , node (physics) , folding (dsp implementation) , representation (politics) , cluster analysis , graph , network topology , algorithm , topology (electrical circuits) , feature learning , vector space , theoretical computer science , artificial intelligence , mathematics , computer network , geometry , electrical engineering , structural engineering , combinatorics , politics , political science , law , engineering
Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space, which can reduce the temporal and spatial complexity of downstream network data mining such as node classification and graph clustering. This paper addresses the problem that neighborhood information-based network representation learning algorithm ignores the global topological information of the network. We propose the Network Representation Learning Algorithm Based on Community Folding (CF-NRL) considering the influence of community structure on the global topology of the network. Each community of the target network is regarded as a folding unit, the same network representation learning algorithm is used to learn the vector representation of the nodes on the folding network and the target network, then the vector representations are spliced correspondingly to obtain the final vector representation of the node. Experimental results show the excellent performance of the proposed algorithm.