Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. In this paper we propose a new method which uses the nodes attributes information along with the topological structure of the network in the clustering process. In order to use the information about the attributes nodes, the collaborative clustering can be employed in the model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple sites by applying different clustering algorithms and therefore improve the final clustering result. The purpose of this article is to introduce a new attributed collaborative multi-view networks based on community detection in networks and topological collaborative learning. The idea consists in modifying databases by adding virtual points which convey clustering information, to change the position of centers of the clustering solution. Experimental results demonstrate the effectiveness of the proposed method through comparisons with the state-of-the-
Collaborative filtering is a well-known technique for recommender systems. Collaborative filtering models use the available preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. Collaborative filtering suffers from the data sparsity problem when users only rate a small set of items which makes the computation of users similarity imprecise and reduce consequently the accuracy of the recommended items. Clustering techniques include multiplex network clustering can be used to deal with this problem. In this paper, we propose a collaborative filtering system based on clustering multiplex network that predict the rate value that a user would give to an item. This approach looks, in a first step, for users having the same behavior or sharing the same characteristics. Then, use the ratings from those similar users found in the first step to predict other ratings. The proposed approach has been validated on MovieLens dataset and the obtained results have shown very promising performances.
Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. First we motivate the interest in the study of this issue. Then we review the main approaches proposed to deal with this problem. We propose a comparative study of some existing attributed network community detection algorithm on both synthetic data and on real world data.
Multiplex network model has been recently proposed as a mean to capture high level complexity in real world interaction networks. This model, in spite of its simplicity, allows handling multi-relationnal, heterogeneous, dynamic and even attributed networks. However, it requiers redefining and adapting almost all basic metrics and algorithms generally used to analyse complex networks. In this work we present MUNA: a MUltiplex Network Analysis library that we have developed in both R and Python on top of igraph network analysis package. In its current version, MUN A provides primitives to build, edit and modify multiplex networks. It also provides a bunch of functions that allow to provide basic metrics about multiplex networks. However, the most interesting functionality provided by MUNA is probably the wide variety of available community detection algorithms. Actually, the library implements different approaches for community detection including: partition aggregation approaches, layer aggregation approaches and direct multiplex approaches such as the GenLouvain and MuxLicod algorithms. It also offer an extended list of multiplex community evaluation indexes.
Recommendation systems provide the facility to understand a person's taste and find new, desirable content for them based on aggregation between their likes and rating of different items. In this paper, we propose a recommendation system that predict the note given by a user to an item. This recommendation system is mainly based on unsupervised topological learning. The proposed approach has been validated on MovieLens dataset and the obtained results have show very promising performances.
Nous nous intéressons dans ce travail au problème de détection de communautés dans les réseaux multiplexes. Le modèle de réseau multiplexe a été récemment introduit afin de faciliter la modélisation des réseaux multirelationnels, des réseaux dynamiques et/ou des réseaux attribués. Les approches existantes pour la détection de communautés dans ce genre de graphes sont, pour la plupart, basées sur des schémas d’agrégation de couches ou d’agrégation de partitions. Nous proposons ici une nouvelle approche centrée graine qui permet de prendre en compte directement la nature multi-couche d’un réseau multiplexe. Des expérimentations effectuées sur différents réseaux multiplexes montrent que notre approche surpasse les approches de l’état de l’art en termes de qualité des communautés identifiées.
