Chain motifs: The tails and handles of complex networks

TL;DR

This paper introduces a new class of network motifs called chains, analyzes their density in different networks, and demonstrates their usefulness in characterizing real-world complex networks.

Contribution

It proposes a novel motif type based on chains of degree-two nodes, along with an algorithm for their detection and analysis in various networks.

Findings

Chains are prevalent in real-world networks like WWW, books, and power grids.

Theoretical analysis of chain density in random and scale-free networks.

Chains can result from incomplete sampling of networks.

Abstract

Great part of the interest in complex networks has been motivated by the presence of structured, frequently non-uniform, connectivity. Because diverse connectivity patterns tend to result in distinct network dynamics, and also because they provide the means to identify and classify several types of complex networks, it becomes important to obtain meaningful measurements of the local network topology. In addition to traditional features such as the node degree, clustering coefficient and shortest path, motifs have been introduced in the literature in order to provide complementary description of the networks connectivity. The current work proposes a new type of motifs, namely chains of nodes, namely sequences of connected nodes with degree two. These chains have been subdivided into cords, tails, rings and handles, depending on the type of their extremities (e.g. open or connected). A theoretical analysis of the density of such motifs in random and scale free networks is described, and an algorithm for identifying those motifs in general networks is presented. The potential of considering chains for network characterization has been illustrated with respect to five categories of real-world networks including 16 cases. Several interesting findings were obtained, including the fact that several chains were observed in the real-world networks, especially the WWW, books, and power-grid. The possibility of chains resulting from incompletely sampled networks is also investigated.