# Identification of intrinsic long-range degree correlations in complex networks

## TL;DR

This paper introduces a method to identify intrinsic long-range degree correlations in complex networks, distinguishing them from those induced by local correlations, and demonstrates its application on real-world networks.

## Contribution

The paper develops a novel approach to extract intrinsic LRDCs by comparing real networks with correlated random models, revealing underlying long-range structures.

## Key findings

- Intrinsic LRDCs exist beyond local degree correlations.
- The method successfully distinguishes intrinsic from induced LRDCs.
- Application to real-world networks validates the approach.

## Abstract

Many real-world networks exhibit degree-degree correlations between nodes separated by more than one step. Such long-range degree correlations (LRDCs) can be fully described by one joint and four conditional probability distributions with respect to degrees of two randomly chosen nodes and shortest path distance between them. While LRDCs are induced by nearest-neighbor degree correlations (NNDCs) between adjacent nodes, some networks possess intrinsic LRDCs which cannot be generated by NNDCs. Here we develop a method to extract intrinsic LRDC in a correlated network by comparing the probability distributions for the given network with those for nearest-neighbor correlated random networks. We also demonstrate the utility of our method by applying it to several real-world networks.