• Aucun résultat trouvé

Networks clustering with bee colony

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Networks clustering with bee colony"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Networks clustering with bee colony

Authors Bilal Saoud Publication date 2019/8/15 Journal

Artificial Intelligence Review

Volume 52 Issue 2 Pages 1297-1309 Publisher Springer Netherlands Description

Clustering of networks, which represent a context, can be very useful. Clusters of a network are called communities and they form a community structure. Many community structure detection methods have been proposed in order to find the community structure. In our study, we propose a new hierarchical method for networks clustering based on bee colony. Our method based on bee colony to split a network into two networks (clusters) which maximize the quality function called modularity. After that we split each clusters (networks) until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities (clusters) into which a network should be divided. Our method was tested on computer-generated networks, real-world networks with and without a known …

Références

Télécharger maintenant ( PDF - 1 Page - 243.00 KB )

Documents relatifs

Bee Colony Optimization for Clustering Incomplete Data

In [27] six methods were compared: Selective Neural Network Ensemble (SNNE), Multi-Granulation Ensemble Method (MGNE) proposed in [26], Neural Network Ensemble method without

The Use of Modularity Algorithms as part of the Conceptualization of the Perspectival Form in Large Networks

Cosac & Naify. The Computational Turn: Thinking About the Digital Humanities. In The Digital Humanities: Beyond Computing. Fast unfolding of communities in large

MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis

After performing clustering, morpheme analyses are obtained based on the word families identified and the transformation rule edges linking words which belong to the same

Modularity maximization to find community structure in complex networks

To evaluate our method to find community structures in networks, we have tested it on computer-generated and several real networks (Zackary's Karate Club [11], Amer- ican

Meanings for Fraction as Number - Measure by Exploring the Number Line

Working within these tools, the dual representation of fractions as numbers and points on the number line, the order of fractions and the role of the unit came into play offering

On the geometry of metric measure spaces. I

A. Alexandrov [1] introduced the notion of lower curvature bounds for metric spaces in terms of comparison properties for geodesic triangles. These curvature bounds are equivalent

On the geometry of metric measure spaces. II

(Generalized Bishop–Gromov volume growth inequality) Assume that the metric measure space ( M, d, m ) satisfies the curvature-dimension condition CD ( K, N ) for some real numbers K, N

This gives us a relation between the Ginzburg-Rallis period and the central value of the exterior cube L-function of GL6

Note that the Levi subgroup L (see subsection 1.4.2) of Q takes each of the types of orbits above to themselves, but moves around the isotropic two-dimensional subspaces V i in