TELKOMNIKA Telecommunication, Computing, Electronics and Control
LPCNN: convolutional neural network for link prediction based on network structured features
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
LPCNN: convolutional neural network for link prediction based on network structured features
LPCNN: convolutional neural network for link prediction based on network structured features
Subject
Convolutional neural network, Deep learning, Heuristic scores, Social network analysis, Structural information
Description
In a social network (SN), link prediction (LP) is the process of estimating
whether a link will exist in the future. In prior LP papers, heuristics score
techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%.
whether a link will exist in the future. In prior LP papers, heuristics score
techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%.
Creator
Asia Mahdi Naser Alzubaidi, Elham Mohammed Thabit A. Alsaadi
Source
DOI: 10.12928/TELKOMNIKA.v20i6.22990
Publisher
Universitas Ahmad Dahlan
Date
December 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Asia Mahdi Naser Alzubaidi, Elham Mohammed Thabit A. Alsaadi, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
LPCNN: convolutional neural network for link prediction based on network structured features,” Repository Horizon University Indonesia, accessed December 22, 2024, https://repository.horizon.ac.id/items/show/4466.
LPCNN: convolutional neural network for link prediction based on network structured features,” Repository Horizon University Indonesia, accessed December 22, 2024, https://repository.horizon.ac.id/items/show/4466.