Degree difference: A simple measure to characterize structural heterogeneity in complex networks

TL;DR

This paper introduces degree difference (DD), a simple local measure for analyzing structural heterogeneity in complex networks, revealing insights beyond traditional global measures like assortativity.

Contribution

The paper demonstrates that DD captures unique structural properties in networks, distinguishes different network types, and relates to network robustness, offering a new tool for network analysis.

Findings

DD distribution differentiates network types.

DD reveals structural roles of edges.

DD correlates with network robustness.

Abstract

Despite the growing interest in characterizing the local geometry leading to the global topology of networks, our understanding of the local structure of complex networks, especially real-world networks, is still incomplete. Here, we analyze a simple, elegant yet underexplored measure, `degree difference' (DD) between vertices of an edge, to understand the local network geometry. We describe the connection between DD and global assortativity of the network from both formal and conceptual perspective, and show that DD can reveal structural properties that are not obtained from other such measures in network science. Typically, edges with different DD play different structural roles and the DD distribution is an important network signature. Notably, DD is the basic unit of assortativity. We provide an explanation as to why DD can characterize structural heterogeneity in mixing patterns unlike global assortativity and local node assortativity. By analyzing synthetic and real networks, we show that DD distribution can be used to distinguish between different types of networks including those networks that cannot be easily distinguished using degree sequence and global assortativity. Moreover, we show DD to be an indicator for topological robustness of scale-free networks. Overall, DD is a local measure that is simple to define, easy to evaluate, and that reveals structural properties of networks not readily seen from other measures.