Complex network classification using partially self-avoiding deterministic walks

TL;DR

This paper introduces a novel measurement based on partially self-avoiding walks for classifying complex networks, demonstrating improved accuracy over traditional measurements on a large dataset.

Contribution

A new measurement for complex network classification using partially self-avoiding walks, reducing correlation issues among traditional measurements.

Findings

Improved classification accuracy with the new measurement.

Validated on 40,000 networks from four models.

Outperforms traditional topological measurements.

Abstract

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification rely on the use of representative measurements that model topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40.000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones.