Document Type
Article
Source of Publication
Procedia Computer Science
Publication Date
1-1-2023
Abstract
Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential decay, we show that the method achieves consistently high performance across various real-world datasets, surpassing other state-of-the-art methods. Our experiments on real-world networks demonstrate that The LCG method proposed in this study exhibits consistently high performance across most of the evaluated datasets, with an average score of 0.95%, surpassing the other methods.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
224
First Page
357
Last Page
364
Disciplines
Computer Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Wasim, Muhammad; Al-Obeidat, Feras; Moreira, Fernando; Gul, Haji; and Amin, Adnan, "Forecasting Networks Links with Laplace Characteristic and Geographical Information in Complex Networks" (2023). All Works. 6127.
https://zuscholars.zu.ac.ae/works/6127
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series