Multi-hop assortativities for networks classification

TL;DR

This paper introduces multi-hop assortativity, a new network fingerprinting method that captures node similarity over paths of various lengths, improving network classification accuracy in social and chemoinformatics benchmarks.

Contribution

The paper presents the concept of multi-hop assortativity, unifying existing measures and providing a versatile tool for network characterization and classification.

Findings

Multi-hop assortativity effectively characterizes networks.

The method outperforms state-of-the-art classification techniques.

Features derived from multi-hop assortativity are highly accurate.

Abstract

Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of 'fingerprints' to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.