A multilevel backbone extraction framework

TL;DR

The paper introduces a multilevel framework for extracting network backbones that improves handling of heterogeneity and preserves essential structures, outperforming classical methods across diverse real-world networks.

Contribution

It presents a versatile, multilevel backbone extraction framework that enhances network analysis by effectively managing heterogeneity and integrating classical backbone techniques.

Findings

Outperforms classical backbone methods on real-world networks.

Effectively preserves key network structures and properties.

Demonstrates versatility across various network types.

Abstract

As networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most significant and structurally influential connections within a network. Network heterogeneity often results in complex and intricate structures, making it challenging to identify the backbone. In response, we introduce the Multilevel Backbone Extraction Framework, a novel approach that diverges from conventional backbone methodologies. This generic approach prioritizes the mesoscopic organization of networks. First, it splits the network into homogeneous-density components. Second, it extracts independent backbones for each component using any classical Backbone technique. Finally, the various backbones are combined. This strategy effectively addresses the heterogeneity observed in network groupings. Empirical investigations on real-world networks underscore the efficacy of the Multilevel Backbone approach in preserving essential network structures and properties. Experiments demonstrate its superiority over classical methods in handling network heterogeneity and enhancing network integrity. The framework is adaptable to various types of networks and backbone extraction techniques, making it a versatile tool for network analysis and backbone extraction across diverse network applications.