C3D-LSTM: a novel convolution-3D-based LSTM for link prediction in dynamic social networks
Issued Date
2024-01-01
Resource Type
ISSN
17411106
eISSN
17411114
Scopus ID
2-s2.0-85188862485
Journal Title
International Journal of Web and Grid Services
Volume
20
Issue
1
Start Page
114
End Page
134
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Web and Grid Services Vol.20 No.1 (2024) , 114-134
Suggested Citation
Chen Y.C., Thaipisutikul T. C3D-LSTM: a novel convolution-3D-based LSTM for link prediction in dynamic social networks. International Journal of Web and Grid Services Vol.20 No.1 (2024) , 114-134. 134. doi:10.1504/IJWGS.2024.137563 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97846
Title
C3D-LSTM: a novel convolution-3D-based LSTM for link prediction in dynamic social networks
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Recently, due to the surge in the use of social networks, link prediction has become an essential technique which could enable service providers to anticipate future friendships between users based on the network structure and personal data so as to enhance consumer loyalty and experience. Undoubtedly, link prediction analysis becomes increasingly difficult when social networks expand quickly, particularly in light of the major advancements in complex social network modelling. Prior studies which predicted social links based on static network settings may have ignored the dynamic variation of networks over time. In this research, an end-to-end model, convolution-3D-based long-short-term memory (abbreviated as C3D-LSTM), is developed to integrate the convolution neural network (CNN) and long-short-term memory (LSTM) network for effective link prediction. We employ 3D convolution to detect subtle patterns in social network snapshots, capturing short-term spatial-temporal features. LSTM layers then interpret these features to model the network's long-term temporal dynamics. To demonstrate its practicability, extensive experiments are conducted to show that C3D-LSTM surpasses current state-of-the-art techniques and delivers remarkable performance.