Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1948 2018 2018 Computer Science Department, Ibb University, Ibb, Yemen; Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Amr Al Al-Furas 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 Al-Adrousy Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Samir Elmougy 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 2023 120 131 10.54216/FPA.120210 https://www.americaspg.com/articleinfo/3/show/1948