Convex skeletons of complex networks

TL;DR

This paper introduces convex skeletons as a new way to extract simplified, backbone-like structures from complex networks, preserving key properties while making shortest paths more unique.

Contribution

It proposes methods for extracting convex skeletons, a generalization of spanning trees, and demonstrates their effectiveness on various empirical networks.

Findings

Convex skeletons retain degree distribution, clustering, and community structure.

They make shortest paths between nodes largely unique.

In coauthorship networks, convex skeletons highlight the strongest ties.

Abstract

A convex network can be defined as a network such that every connected induced subgraph includes all the shortest paths between its nodes. Fully convex network would therefore be a collection of cliques stitched together in a tree. In this paper, we study the largest high-convexity part of empirical networks obtained by removing the least number of edges, which we call a convex skeleton. A convex skeleton is a generalisation of a network spanning tree in which each edge can be replaced by a clique of arbitrary size. We present different approaches for extracting convex skeletons and apply them to social collaboration and protein interactions networks, autonomous systems graphs and food webs. We show that the extracted convex skeletons retain the degree distribution, clustering, connectivity, distances, node position and also community structure, while making the shortest paths between the nodes largely unique. Moreover, in the Slovenian computer scientists coauthorship network, a convex skeleton retains the strongest ties between the authors, differently from a spanning tree or high-betweenness backbone and high-salience skeleton. A convex skeleton thus represents a simple definition of a network backbone with applications in coauthorship and other social collaboration networks.