Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques

Abstract

Networks are an invaluable tool for representing and understanding complex systems. They offer a wide range of applications, including identifying crucial nodes, uncovering communities, and exploring network formation. However, when dealing with large networks, the computational challenge can be overwhelming. Fortunately, researchers have developed several techniques to address this issue by reducing network size while preserving its fundamental properties [1-9]. To achieve this goal, two main approaches have emerged: structural and statistical methods. Structural methods aim to keep a set of topological features of the network while reducing its size. In contrast, statistical methods eliminate noise by filtering out nodes or links that could obscure the network's structure, utilizing advanced statistical models. In a previous work [10] we compared a set of seven statistical backbone filtering techniques in the World Air Transportation network. Results show that the Marginal Likelihood Filter, Disparity Filter, and LANS Filter give more importance to high-weight edges. The other techniques emphasize both small and high-weighted edges.
Fichier principal
Vignette du fichier
FRCCS_2023_ALI_1.pdf (422.64 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04054954 , version 1 (01-04-2023)

Identifiers

  • HAL Id : hal-04054954 , version 1

Cite

Ali Yassin, Hocine Cherifi, Hamida Seba, Olivier Togni. Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques. French Regional Conference on Complex Systems 2023, CSS France, May 2023, Le Havre, France. ⟨hal-04054954⟩
46 View
32 Download

Share

Gmail Facebook X LinkedIn More