Mapping change in large networks

TL;DR

This paper introduces a bootstrap resampling and significance clustering method to identify meaningful structural changes in large networks, demonstrated by analyzing scientific citation patterns over a decade.

Contribution

The authors develop a novel approach combining bootstrap resampling with significance clustering to distinguish real network changes from noise.

Findings

Neuroscience evolved from interdisciplinary to a standalone discipline.

Alluvial diagrams effectively visualize significant network changes.

Method successfully identifies meaningful structural shifts in large-scale networks.

Abstract

Change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: Interactions within and between organisms change; transportation patterns by air, land, and sea all change; the global financial flow changes; and the frontiers of scientific research change. Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. Here we show that bootstrap resampling accompanied by significance clustering provides a solution to this problem. To connect changing structures with the changing function of networks, we highlight and summarize the significant structural changes with alluvial diagrams and realize de Solla Price's vision of mapping change in science: studying the citation pattern between about 7000 scientific journals over the past decade, we find that neuroscience has transformed from an interdisciplinary specialty to a mature and stand-alone discipline.