aspg-3-1948-1791942836 20261014015356 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2023 12 2 Amr Al-Furas Computer Science Department, Ibb University, Ibb, Yemen; Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Mohammed F. Alrahmawy Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Waleed Mohamed Al-Adrousy Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Samir Elmougy Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Complex networks are a diverse set of networks found in various fields, such as social, technological, and biological networks. One important task in complex network analysis is link prediction, which involves detecting missing links or predicting future link formation. Many methods based on network structure analysis have been developed for link prediction, including network representation learning (NRL) models that represent nodes in a low-dimensional space. Fusion-based attributed NRL methods are particularly effective, as they capture both content and structure information. However, NRL models for link prediction are binary classification models, which face challenges in identifying negative links and prioritizing predicted links. To address these challenges, we propose a novel approach that treats link prediction as a novelty detection problem. Our approach uses the Local Outlier Factor (LOF) algorithm to quantify the novelty of non-existent links based on the representations of existing links. Our experimental results show that our proposed approach outperforms existing methods, particularly when used with fusion-based attributed NRL models 2023 120 131 1948 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.120210 https://americaspg.com/journal/3/article/1948 https://americaspg.com/storage/01689685540.pdf