[Paper Review] Two Decades of Network Science as seen through the co-authorship network of network scientists

Roland Molontay, Marcell Nagy|arXiv (Cornell University)|Aug 22, 2019

Complex Network Analysis Techniques25 references4 citations

TL;DR

This paper analyzes the co-authorship network of 52,406 network scientists over 20 years (1999–2019) to map the evolution of network science as a multidisciplinary field. Using structural network analysis and community detection, it reveals a globally interconnected yet regionally clustered community, with strong collaboration from China, the US, and Europe, and finds high correlation between network centrality and scientometric impact.

ABSTRACT

Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barabási & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on the community structure of complex networks, respectively. These fundamental papers initiated a new era of research establishing an interdisciplinary field called network science. Due to the multidisciplinary nature of the field, a diverse but not divided network science community has emerged in the past 20 years. This paper honors the contributions of network science by exploring the evolution of this community as seen through the growing co-authorship network of network scientists (here the notion refers to a scholar with at least one paper citing at least one of the three aforementioned milestone papers). After investigating various characteristics of 29,528 network science papers, we construct the co-authorship network of 52,406 network scientists and we analyze its topology and dynamics. We shed light on the collaboration patterns of the last 20 years of network science by investigating numerous structural properties of the co-authorship network and by using enhanced data visualization techniques. We also identify the most central authors, the largest communities, investigate the spatiotemporal changes, and compare the properties of the network to scientometric indicators.

Motivation & Objective

• To map the evolution of the network science community through its co-authorship network over the past two decades.
• To investigate structural properties such as degree distribution, centrality, and modularity in the co-authorship network.
• To identify key communities, collaboration patterns, and spatiotemporal dynamics in network science research.
• To compare network-based centrality measures with traditional scientometric indicators like citation counts.
• To assess the international collaboration landscape and the role of major countries and institutions in shaping network science.

Proposed method

• Collected 29,528 network science papers citing three seminal works (Watts & Strogatz, Barabási & Albert, Girvan & Newman).
• Defined 'network scientists' as authors with at least one paper citing any of the three milestone papers.
• Constructed a co-authorship network of 52,406 network scientists, with edges representing co-authorship on at least one paper.
• Applied the Clauset-Newman-Moore greedy modularity maximization algorithm to detect communities in the network.
• Used edge-weighted country networks to visualize international collaboration patterns based on co-authored papers.
• Correlated network centrality measures (e.g., degree, betweenness) with citation counts to assess scientometric alignment.

Experimental results

Research questions

• RQ1 How has the co-authorship network of network scientists evolved in terms of size, connectivity, and topology over the past 20 years?
• RQ2 What are the dominant research areas and geographical distributions within the largest communities of network scientists?
• RQ3 How do network centrality measures correlate with traditional scientometric indicators such as citation counts?
• RQ4 What are the patterns of international collaboration, and which countries or regions are most active in cross-national research?
• RQ5 How have the structural properties of the co-authorship network, such as modularity and giant component size, changed over time?

Key findings

• The co-authorship network includes 52,406 network scientists and 29,528 papers, with a giant component of 32,904 nodes (62.8% of total).
• The largest community (14,136 authors) is dominated by Chinese physicists, followed by strong US and European presence.
• The top 10 communities show high homogeneity: e.g., the 10th largest is 93% neuroscientists and 89% from the USA.
• China leads in total network science publications, while US scientists are most active in international collaborations.
• European countries exhibit dense internal collaboration, forming a well-connected regional cluster in the international collaboration network.
• A strong correlation exists between network centrality measures (e.g., degree, betweenness) and citation counts, indicating alignment with traditional scientometric indicators.