Fundamental statistical features and self-similar properties of tagged networks

TL;DR

This paper explores the statistical features and self-similar properties of tagged networks, introducing new measures like tag-assortativity and node uniqueness, and analyzing their relation to network topology across different complex systems.

Contribution

It introduces novel concepts such as tag-assortativity and node uniqueness, and demonstrates their applicability to understanding the structure of diverse tagged networks.

Findings

Networks are generally tag-assortative, indicating universal tag-topology relations.

Node uniqueness distribution varies across networks, revealing system-specific features.

Networks exhibit scale invariance in topology and tag distribution, characterized by the tag-assortativity exponent.

Abstract

We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterise the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g., the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterised by the tag-assortativity exponent, which is specific to each system.