Source of Publication
Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy.
complex network analysis, complex networks, hill climbing, link prediction, local link prediction methods
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Gul, Haji; Al-Obeidat, Feras; Amin, Adnan; Moreira, Fernando; and Huang, Kaizhu, "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs" (2022). All Works. 5506.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series