L’Apprentissage Automatique Pour La Prédiction De Lien Dans Les Réseaux Complexes

Abstract

This work explores graph theory concepts to model and analyze complex networks with an emphasis on the use of machine learning. Methods examined include similarity measures based on common neighbors, measures based on the length of paths, We also evaluated the effectiveness of different classification algorithms, such as Support Vector Machine (SVM), K Nearest Neighbors (KNN)…Our results show that certain combinations of these methods and algorithms make it possible to obtain accurate predictions of link classes in complex networks, thus opening new perspectives for their analysis and application in various fiel