Multi-hop assortativities for network classification. (18th December 2018)

• Record Type: Journal Article
• Authors: Gutiérrez-Gómez, Leonardo; Delvenne, Jean-Charles
• Editors: Perc, Matjaz
• Is Part Of: Journal of complex networks. Volume 7:Number 4(2019)
• Page Start: 603
• Page End: 622
• Publication Date: 2018-12-18
• Subjects: network classification -- multi-hop assortativities -- graph classification; Numerical analysis -- Periodicals; Computer networks -- Periodicals; Social networks -- Periodicals; 518.05
• DOI: 10.1093/comnet/cny034
• Languages: English
• ISSNs: 2051-1310

Abstract

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.