Is academia becoming more localised? The growth of regional knowledge networks within international research collaboration

Following advances in transportation and communication technology over the past centuries, we’re witnessing a rise in global interactions both in terms of cross-border trade and investment as well as flows of people and information. Termed globalisation, this complex process involves interaction and integration of people, businesses, and governments and while it primarily concerns economic aspects, social and cultural dimensions are similarly salient. In parallel to the process of globalisation, ongoing global economic restructuring has resulted in a transition towards a knowledge-based economy, where a ‘greater reliance on intellectual capabilities than on physical or natural resources’ (Powell and Snellman 2004) has meant a rise in production and services that are based on knowledge-intensive activities. The importance of specialised skills along with knowledge and information as forms of non-physical capital has grown, since economic growth increasingly derives from intangible intellectual property including copyrights, patents, trademarks, and trade secrets that work to make more effective use of inputs and available resources. Knowledge diffusion and innovation underpin competition and fuel economic growth at almost all stages of development, as well as play a critical role in enabling responses to complex economic, environmental, and social challenges. Domestic and global research communities are central players in creating and diffusing knowledge and contributing to the development of new products and processes. These communities comprise of research and development activities, research laboratories, universities, and other educational institutions that, together with partners in the private sector and government, form innovation ecosystems (Jackson 2011).

The role of innovation, science and technology as drivers of economic growth and as vital enablers of sustainability is highlighted in the recent UNESCO (2015) Science Report, which showcases the trajectories of a large number of countries ‘incorporating science, technology, and innovation in their national development agendas, in order to be less reliant on raw materials and move towards knowledge economies.’ The desirability of fostering local skills and capacities for economic development is similarly echoed by recent work in economic complexity and economic geography (Hidalgo et al. 2007; Frenken and Boschma 2007; Hidalgo and Hausmann 2009; Hausmann and Hidalgo 2011; O’Clery et al. 2019a), analysing the growth of cities and regions. This literature finds the availability of diverse knowledge capacities or complex skills and capabilities as central to the development trajectories of regions, countries, and cities. They conceptualise knowledge and capabilities as geographically ‘sticky’, since tacit knowledge and abilities are a result of a workforce with skills learned on-the-job, and are thus not easily transportable. Research collaboration, in particular with academics from other regions likely in possession of novel or complementary skills and capabilities, could allow countries to upgrade their academic capacity and respond to unique societal and economic challenges more readily.

As countries and regions find themselves at various stages of the transformation towards—and readiness to join—the global knowledge economy (Ojanperä et al. 2017, 2019), the creation of scientific knowledge is more important than ever. Public and private sector funding is directed towards developing domestic research capabilities, and countries are putting policies in place to attract scientific talent from abroad (UNESCO 2015). The OECD Development strategy, implemented in partnership with the United Nations and the World Bank, as well as the OECD policy frameworks for Tertiary Education, Innovation, Development, and Gender Equity, all call for the promotion of regional and international research networks in order to further the dual pursuit of research communities everywhere, summarised by the Programme on Innovation (2012) as: ‘knowledge generation per se and their specific role in attaining national development priorities’.

Reflecting this trend, the number of researchers and publications has been growing, with a 20 percent increase between 2007 and 2014 (UNESCO 2015). The extent of scientific collaboration has increased in parallel, both overall (Wagner-Döbler 2001; Meyer and Bhattacharya 2004), and internationally, between researchers based in different countries (Narin et al. 1991; Wagner and Leydesdorff 2005a; Wuchty et al. 2007; Jones et al. 2008; Gazni et al. 2012). Various factors have been suggested as underpinning the growing propensity to collaborate, including advancements in technologies facilitating remote collaboration (Ding et al. 2010), policy initiatives and funding schemes to encourage international collaboration (Frenken et al. 2009; Ubfal and Maffioli 2011), specialisation requiring collaboration with researchers who may not be available within the local talent pool, cultivation of research impact and credibility (Kumar 2015), and avoidance of duplicating research efforts (Katz and Martin 1997).

Indeed, the internationalisation of research collaborations has received increasing attention over the past few decades. Collaboratively authored research has higher impact than research published by a sole author, both in terms of number of publications (Katz and Martin 1997; Lee and Bozeman 2005; Wuchty et al. 2007) and citations (Sooryamoorthy 2017; Gazni and Didegah 2011), while research published by international author teams tends to attract more citations than research authored by national teams (Narin et al. 1991; Katz and Martin 1997; Frenken et al. 2005). Furthermore, Jones et al. (2008) show that multi-university collaborations produce the highest impact papers when top-tier universities are included, and are increasingly stratified by in-group university rank.

An emergent body of literature on research collaboration networks—reviewed below—has primarily investigated ties between individuals or institutions, often focusing on particular disciplinary communities or bounded by a regional or sub-national context. Few studies, however, have looked in detail at changing patterns of international collaboration focusing on bilateral ties at the country level, and including all major disciplines. Instead studies tend to focus on particular disciplines, such as medicine or the life sciences. We look at research collaboration across all major disciplines, as it reflects the broad creation and diffusion of knowledge, which contributes to the development of new products and processes, or innovation across economic, social, and political domains.

The existing body of research on international research collaboration networks has deployed a variety of network methods, including network visualisation, local network measures focusing on the importance of nodes, models explaining network growth, and regression methods. In the present study, we apply a range of sophisticated methods deriving from network science and mathematical modelling, including historical profile clustering, calculation of the entropy of collaborations, community detection, and mutual information comparisons, which allow us to uncover patterns that have previously remained opaque.

Further, where studies have analysed a time period rather than investigated a snapshot, the time window tends to not span more than a decade or two. We address this research gap through analysing a dataset of international collaboratively authored scientific publications covering a range of disciplines published between 1970 and 2018. In doing so, we assess the extent to which countries learn from each other through ‘borrowing’ capabilities and specialisms from colleagues in other countries or regions, and thus induce knowledge flow. In the analysis to follow, we exploit a variety of network and mathematical modelling tools to analyse the temporal evolution of the global collaboration network to reveal what we term ‘knowledge basins’ [a concept related to ‘skill basins’ as proposed by O’Clery et al. (2019b)]. These are groups of countries which tend to collaborate frequently internally, but less frequently with other groups, thus forming localised (and potentially isolated) clusters of research output. These clusters evolve over time, aligning with colonial and historical geopolitical alliances pre-2000, but coalescing more along geographical or regional lines since 2000.

The remainder of this paper is organised as follows. Section 2 will survey the relevant research on co-authorship networks. Our choice of data will be elaborated upon in Sect. 3, while Sect. 4 will introduce some preliminary analysis of the data. In Sects. 5 and 6 we will present our main research methodology and results with some discussion. Finally, Sect. 7 summarises our contribution, discusses the implications of our findings, and proposes avenues for future work.

The literature on research networks has its roots in scientometrics, a sub-field of bibliometrics measuring and analysing scientific literature, but it additionally draws from related disciplines of information systems, information science, and science of science. While the creation of the Science Citation Index in 1964 and related studies (Burton and Kebler 1960; Garfield and Sher 1963; Kessler et al. 1962; Osgood and Xhignesse 1963; Price 1963; Tukey 1962) were seminal in establishing the field, the pioneering article by Price (1965) was the first one to investigate networks of scientific papers, and found that the network under study was scale-free with the in-degree (citations within an article) and out-degree (citations to an article) having power-law distributions. Since these early studies’ focus on citation networks, the literature has branched out to comprise research on varied themes such as co-citation networks (documents are connected if they appear together in a reference list), co-word networks (words are connected if they appear together within a document), research collaboration (in particular through co-authorship of documents or collaborative grants), researcher mobility, and institutional boundaries. While these studies investigate varied topics, some themes that have received substantial research attention include identifying research fronts, evaluating the impact of individual authors in comparison to collaborations, and the relative influence of disciplines and journals.

Our dataset contains all co-authorship relations between authors of documents published between 1970 and 2018 which are indexed in Scopus. We chose Scopus as our data source because it has a high level of accuracy as is characteristic for bibliographic databases (Gusenbauer 2019; Gusenbauer and Haddaway 2019). It also has wide geographic, disciplinary, and temporal coverage including 24,600 active titles and 5000 publishers of scientific journals, books, and conference proceedings across the fields of science, technology, medicine, social sciences, and arts and humanities (Elsevier 2020). Since we sought as comprehensive a dataset as possible, we decided not to consider the academic search engines because, while they are able to access the largest number of records, the query functions for them seem to be unreliable for detailed bibliometric data such as author affiliation (Mingers and Meyer 2017; Gusenbauer 2019). Similarly, while the aggregate services ProQuest and EbscoHost and the bibliographic database Web of Science provide more accurate results, it was not apparent whether our institutional access to these services would cover all constituent databases [a well-known shortcoming of these services (Gusenbauer 2019)].

While there are obvious advantages to using the Scopus dataset, there are nonetheless several known limitations including weaker coverage for the social sciences and humanities, and non-English publications (Aksnes and Sivertsen 2019). However, some have argued (Bennett 2013) that English has come to dominate academia as a ‘lingua franca’ leading to erosion of scholarly discourses in other languages and possibly introducing preferences for certain kinds of knowledge (Trahar et al. 2019). While quantifying this trend isn’t possible within the scope of this analysis, it is likely to introduce a shift in original contributions from other languages to English over time and thus might increase the representativeness of our data. Furthermore, while Scopus does not include all possible academic outputs, the categories indexed are arguably some of the most salient kinds of academic outputs, and we would not expect that other omitted categories of outputs would introduce a specific geographic bias into our findings.

Since we are interested in collaborative relationships on a country level and across scientific disciplines, we first produce a dataset including all publications with authors in multiple countries (including papers with authors affiliated to multiple institutions in different countries), and aggregate this data to form yearly counts of co-authorship relations between countries based on the geographical location of each author’s institution.¹ Specifically, if a paper or book is affiliated with institutions from more than two countries, e.g., Norway, UK, and India, three co-authorship relations will be included in this dataset: Norway–UK, Norway–India, and UK–India (which could be regionally aggregated to one within Europe co-authorship relationship and two between Europe and Asia co-authorship relationships). Subsequently, we further aggregate the data into ten time periods: 1970–1974, 1975–1979, 1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The final time period does not include 2019 as the Scopus database for this year is as of yet incomplete.²

The production of academic publications is highly unequally distributed geographically. Figure 1a shows that the highest volume of publications is currently authored in the United States, United Kingdom, Germany, China, and India, while the lowest numbers can be found within Africa and Latin America. Looking back over the past five decades, Fig. 1b reveals that Asia is catching up with Europe and the Americas, while the growth of academic publishing is much slower for Africa and Oceania. We contrast this with the growth of co-authored publications and find that growth was much faster in Europe than other continents, in particular after the turn of the century. While Asia is catching up to the Americas, international collaborations are growing much slower than its share of overall publications.

Figure 1d displays the rank of countries in terms of the number of academic publications in 1970, 1985, 2000, and 2015. We observe that while countries in Europe and Asia as well as the United States and Canada are topping the list, some emerging economies such as China, Korea, Iran, and Malaysia significantly increased in rank towards the end of the time period.³

It is clear that national publication and co-authorship rates have been subject to significant change over the past decades. Before proceeding to disentangle co-authorship patterns over time, we desire a simple method to systematically uncover which countries are emerging as research leaders in terms of publication growth (relative to size). To do so, we follow the method described in Gargiulo et al. (2016). First, we calculate the relative abundance of publications of each country within each time-step. That is, at each time-step, we compute the global share of publication activity of country i:where \(n_i^{(t)}\) denotes the total number of publications produced by country i in time period t. However, as shown in Fig. 2a for the countries Iraq, the UK, and Greece, it is a poor measure to compare the historical profiles of countries with dramatically different levels of production. To overcome this, we normalise each country’s relative abundance profile by its total production across the full time period to obtain a measure of average prevalence:To ensure fair comparison, here we require each country to have produced more than 100 publications in each and every time period.⁴ Figure 2b displays this metric for the same three countries, and now the relative trajectories of each country is clear: the UK has slowly declined in relative publication share, while Greece proportionally increased until 2010 (the subsequent decline may be due to the imposition of austerity). Iraq steeply declined in relative publication share from 1990 (possibly due to conflict after the invasion of Kuwait) and has only recovered more recently.

We then use these profiles to cluster countries with similar historical trajectories. We first calculate the Kolmogorov–Smirnov distance between each pair of country profiles [the supremum difference between the cumulative distribution of each profile (Smirnov and Smirnov 1939)], then use this distance matrix as the input for an agglomerative clustering algorithm. This algorithm works by first setting the maximum number of clusters to six (by looking at the corresponding clustering dendrogram), then finding the minimum threshold r such that the distance between any two points within each cluster is less than r, and there are at most six clusters—see e.g., Müllner (2011). These clustered profiles are displayed in Fig. 2c, where each line corresponds to the average prevalence value of a cluster—note that as such Clusters 1 and 3 seem comparable, but the variance of profiles within Cluster 3 is much greater.

In Fig. 2d we display a map of the world coloured by these clusters. Five profiles are typical: the blue cluster (Cluster 1) corresponds to countries with reasonably stable profiles over the period investigated, such as Norway and much of the Global North. The green and yellow clusters (Clusters 2 and 3) include countries with periods of relative growth and decline, such as the UK and Greece. Finally, the red and purple clusters (Clusters 4 and 5) correspond to countries that have greatly increased their publication share in recent years such as Iraq. Amongst these, every region of the Global South has countries which have considerably improved their trajectory in recent times, from Colombia in South America to China in Asia.

The international research landscape is clearly undergoing continued structural change with new leaders emerging from all corners of the globe. Here we ask, how has this shift in the geographic spread and dynamics of knowledge production shaped a re-configuration of cross border collaboration ties?

We wish to quantify how countries have changed their patterns of collaboration over time, focusing particularly on neighbouring and distant ties. One way to do this is to measure a countries’ diversity of links to collaboration partners both within their own region and with countries in other regions.

In order to do this, we first calculate the Shannon entropy (see e.g., Evans et al. (2011); Kumar et al. (1986)) of the distribution of collaboration partners for each country. This provides us with a measure of the spread of collaborations for each country: values closer to one correspond to countries collaborating evenly with many countries around the world, and low values correspond to a narrow focus on collaboration with few countries. To be specific, we define the collaboration entropy for country i aswhere N is the total number of countries in our dataset in the time period,⁵ andwhere \(n_{ij}^{(t)}\) is the number of collaborations of academics from country i with those in country j in time period t.

We are interested in investigating whether countries are collaborating more diversely within their region compared to outside their own region (continent). Hence, we decompose CE as follows:where \(J_u\) is the set of countries in the region of country i, and \(J_o\) is the set of countries outside the region of country i. Diversity increasing within regions when compared to diversity between regions suggests stronger regional clustering, and impacts a variety of network measures analysed later. In particular, if the total strength of internal collaborations relative to external collaborations also increases (as verified in “Appendix 2” and shown in Fig. 6), it implies the formation of localised regional collaboration networks, or knowledge basins within which knowledge circulates more easily.

We plot \(CE_{in}\) versus \(CE_{out}\) for the final time period for all countries in Fig. 3a, where the size of the points is scaled by the total number of collaborations. We observe that European countries (shown in red) seem to collaborate more diversely with each other than with the rest of the world, while for many African countries (shown in blue) the reverse seems to be the case.

In order to assess the dynamics of inter- and intra-region collaboration diversity over time, we compute the proportion of within-region entropy (as a share of total entropy) for each country:We plot the mean value—across countries in a region—of this quantity over time in Fig. 3b. We may observe that within-region diversity was high but has been slowly declining in Europe since 1995, suggesting the region is broadening its focus to some extent. On the other hand, within-region collaboration diversity increased significantly for the Americas and Asia from the 1980s, and for Africa after 1990. However, there appears to be a general small decline in within-region diversity (relative to out-of-region collaboration diversity) in the final two time periods for the Americas, suggesting a recent opening up of their collaboration networks.

The change in research focus, from international to regional collaboration, observed in the previous section provokes a more general investigation of how knowledge flows (as proxied by academic collaborations) may have changed over time. In particular, we ask whether these trends have translated into an overall consolidation of regional ties, creating isolated clusters or pools of knowledge production.

To uncover the complex structure of these flows, we construct a network where the nodes are countries, and the edges correspond to the number of collaborations between countries i and j at time t, \(n_{ij}^{(t)}\), such that the network at this time has adjacency matrix, \(A^{(t)}\), with the corresponding i, jth entries.

Prior to further analysis, to immediately visualise significant partnerships, we follow a similar procedure to that proposed by Neffke and Henning (2013) for estimating skill-overlap between industry pairs based on inter-industry job transitions. The logic behind doing so is similar to that for revealed comparative advantage (RCA, see e.g. Balassa (1965)), in that measures calculated on the network formed by raw counts are typically dominated by those locations with the highest overall production (i.e. USA, China and similar). Instead, we normalise the observed counts by the capacity of each country, measured by total collaborations, using a configuration model-like approach, apply a transformation to help account for the spread of subsequent results, and finally apply a thresholding step. The details may be found in “Appendix 3”.

In Fig. 4, we display this transformed network, with edges with strengths given by Eq. (15), for the 5-year periods commencing in 1970, 1985, 2000, and for 2015–2018, where countries which belong to the same continent have the same colour, and the size of each country is proportional to their total number of publications within that time period. The spring algorithm ForceAtlas in Gephi is used to layout each network, and edges above a 0.5 threshold are shown.

We observe that countries tend to cluster together geographically in latter time periods. This can be seen with respect to the United Kingdom and Germany: in earlier time periods they occupy fairly central ‘positions’, but in the latter time periods locate more closely to other European countries. On the other hand, while we note that the rise of publication volume in China and India is visible particularly over the past two decades, the positions of these countries along with Japan remain relatively close to their regional groups. In Fig. 4e, we display the mean edge weights between the five continents in the form of aggregated adjacency matrices. We observe the emergence of a defined diagonal from the year 2000, while the off-diagonals grow paler. This indicates that intra-regional collaborations have strengthened, while the inter-continental collaborations appear to decline. Once again, we observe that in the most recent time period this trend may be beginning to change, with more intercontinental partnerships emerging.

In order to explore the increasing ‘regionalisation’ of research collaboration, we wish to extract information from the networks about groups of countries engaged in intense research collaboration across time. Exploring such groupings is a key focus of network science, known as community detection. Loosely speaking, this corresponds to a partition of nodes into communities for which within-community links are significantly stronger that between-community links. It is often found to be the case that these naturally arise in the real world, e.g. in social, neurological, or indeed academic networks as under consideration here (Newman and Girvan 2004). Here, such communities reveal groupings of countries which engage in significant research collaboration—and analysis of their evolution over time enables us to extract a quantitative description of the changing global research landscape.

While a variety of methods exist (see e.g. Javed et al. (2018) for an overview), the approach we take is that of optimisation of linearised stability (Delvenne et al. 2010; Lambiotte et al. 2008, 2011). Given a partition X, this method involves computing a sum of the deviations of the network edges within each community from a weighted configuration null model (where edges are shuffled randomly but node strengths are preserved). Mathematically,whereare respectively the strength of node i and total edge weight of the network, \(x_{i}\) is the community of node i (thus \(\delta (x_{i}, x_{j}) = 1\) if i and j are in same community and is zero else), and \(\gamma\) is a so called ‘resolution’ parameter. This final parameter controls the contribution of the null model to the sum, and so affects which partition will be optimal—larger values favour recovering smaller communities, and vice versa. Under the configuration null model, the expected strength of link between i and j is \(k_{i}k_{j}/2m\)—i.e., the total strength of node j times the probability of connecting to node i. In particular, using this null model, if \(\gamma =1\) linearised stability is identical to the conventional Newman-Girvan modularity (Newman and Girvan 2004). This linearised form is also effectively identical to another method previously introduced in Reichardt and Bornholdt (2006) for modularity at different network scales. This tuning parameter is highly useful, as it allows us to avoid to some extent the resolution limit that typical modularity has been shown to face (Fortunato and Barthelemy 2007), in that it is possible to fail to detect non-trivial small communities.

The principal idea behind stability is that if we follow walkers around the network, which jump between nodes with a probability proportional to the edge weight, then over time sets of nodes where walkers spend a prolonged period suggest denser connections within such a set than to outside, i.e. they form form a community. The period of time for which we track such walkers naturally leads to the resolution parameter \(\gamma\). More details on this are provided in “Appendix 4”.

In order to find a node partition X which maximises this function, a typical approach is to use a greedy algorithm by Blondel et al. (2008). This works by initially placing each node in its own community, then iteratively merging nodes with those adjacent to themselves if an increase in linearised stability is achieved. This process is stochastic in the sense that it may produce a slightly different optimum partition depending on the order in which nodes are ‘visited’. It is efficient as only local information (nearest neighbours) to the node is necessary at each step. Recently there has been a further improvement with a similar logic, known as the Leiden algorithm (Traag et al. 2019): this appears to result in higher linearised stability with lower computational cost, and so will be used here. Through studying the variation of information (a metric for comparing partitions) as described in “Appendix 4”, we find that two resolution times \(\tau =1/\gamma\) of interest are \(\tau =1.0\) (i.e. actually conventional modularity) and \(\tau =0.76\), which provides a finer-grained view of the network.

We display the best partitions \(X^{(t)}\) found from applying this optimisation process, with \(\tau =1.0\), to the network constructed for each time period in Fig. 5e. Following a similar approach to that of Pietilänen and Diot (2012) and Fagan et al. (2018), ’flows’ between two communities A and B are scaled according to the Jaccard index \(J(A,B)=|A\cap B|/|A\cup B|\). We first assign each community a colour arbitrarily, then compare adjacent time periods and retain the previous colour if \(J(A,B)>0.6\). The white community corresponds to countries outside of the time period under consideration. This figure contains a wealth of information, for instance evidencing that collaboration patterns often changed more regularly in earlier, more turbulent decades, before beginning to settle from 1995 onwards. It may be seen for example that Europe consolidates as a block at this scale from 1995 onwards, shortly after the formation of the European Economic Area (EEA). We observe that in the final time period, there are four communities which roughly correspond to the regions of Europe and Latin America, North America with China, Australia and nearby countries, and the rest of the world. The community of North America et al. may be an artefact of the USA and China being the two major global producers, and suggests that an alternative null model could be more suitable depending on the goal of analysis—we explore the deviation from the null model further in “Appendix 5”, but leave the development of such an alternative to future work.

In order to further investigate the rate of change of the modular structure over time, and the observed ‘regionalisation’ of research collaboration ties, we wish to quantify the similarity between each partition and its preceding partition, and between each partition and the ‘continental partition’ (where countries are assigned to a community based on continent). While the Jaccard index is good measure for comparing pairs of communities, to compare partitions we instead calculate the normalised mutual information (also known as the symmetric uncertainty (Witten and Frank 2002)). This is defined byfor two partitions X and Y, where n is the number of nodes, and \(p_{i}=|X_{i}|/n\) (the share of nodes in community i of X), \(q_{i}=|Y_{i}|/n\) (the share of nodes in community i of Y), and \(r_{ij}=|X_{i}\cap Y_{j}|/n\) (the share of nodes in both community i of X, and community j of Y).

We compare the partitions obtained in adjacent time-steps through calculating the normalised mutual information: i.e. \(NMI(X^{(t)},X^{(t+1)})\), where \(X^{(t)}\) is the partition obtained for time period t. In Fig. 5f, we display the values of this function over time at two different scales, with \(\tau =1.0\) shown solid, and the finer scale \(\tau =0.76\) shown dashed. We observe that after an initial period of change, recent years have seen relatively stable global research communities form at the finer scale, while at the more aggregate scale there is still some change (primarily due to splits in the large, ‘rest of the world’, community shown in purple). Next, we construct a new partition, C, which divides the world into five continents (communities): each country is assigned to their continent, i.e. Africa, America, Asia, Europe, and Oceania. In order to see how similar each partition is to this continental partition, we calculate \(NMI(X^{(t)},C)\) for all t. Figure 5g confirms what we had suspected from previous figures in that there has been a clear trend towards regionalisation of research ties at both scales, particularly between 1990–2010.

As a final check, we compare the stability of each detected partition to the stability of the continental partition. Since stability is a measure of partition quality, we would expect the stability of the continental to approach that of the detected partition in latter time periods. It is important to understand the difference in quality between these partitions, particularly as there is inherent randomness to the optimisation algorithm used, and it only guarantees convergence to a local optima. In other words, the ‘optimal’ partition we find could in fact be only marginally better than the continental partition in early decades, even if the partitions themselves were very different as measured by NMI. We cannot compare raw values of stability across time, as it varies with respect to network size/density etc.—as such, we compute the ratioThe ratio of the stability scores tells us how well the geographic (or continental) partition ‘performs’ as a set of communities compared to those detected by our community detection algorithm. Figure 5h confirms that, as expected, this ratio declines over time. More specifically, we observe that the continental partition was of significantly lower quality in earlier time periods, particularly for the scale with \(\tau =1.0\), suggesting this was not a good ‘description’ of the network structure at that time. In later periods, the ratio approaches 1 (shown dashed red) at both scales, suggesting that the continental partition is increasingly a good fit for the network structure.

The creation and diffusion of knowledge between nations is crucial for the advancement of skills and capabilities, critical drivers of economic development. Patterns of knowledge diffusion via research collaboration on a global level, however, remain poorly understood. We address this research gap through analysing a worldwide dataset of international scientific publications spanning all major disciplines over five decades. We find that collaboration ties appear to have become more localised since 2000, with researchers prioritising regional co-authorship relative to more distant ties. We corroborate this insight via an analysis of the evolving modular structure of the global collaboration network, finding a recent stabilisation of research clusters along increasingly regional lines.

These findings were unexpected given the generally accepted wisdom on the onward march of globalisation, and thus have a number of significant implications. On one hand, this could be a positive signal: research expertise is growing in many previously under-equipped nations and regions, and hence scholars no longer have to look as far afield as they once did. Regional research efforts may be driven by resident researchers focusing their efforts on addressing particular economic, social, and political concerns within the region. Specific research programmes have been introduced to strengthen scientific collaboration within regions such as the Horizon 2020 (soon to give way to Horizon Europe) initiative in Europe. Further, regional research and development programmes increasingly make use of the ‘smart specialisation’ model in research, whereby countries with well-defined domains of specialisation (e.g., in research and innovation) are seen as more likely to produce research excellency in specific areas. These countries are then chosen as sites for related regional research programmes and institutes, with the aim of anchoring and nurturing these localised sites of expertise. This model was originally developed by the European Union in order to address a transatlantic gap in R&D but has since been adopted by many regions and countries (Gómez Prieto et al. 2019)—a trend that our research findings would seem to support and perhaps a driving force behind some of the patterns we have identified. On the other hand, such a retrenchment may be worrying, given what we know about the importance of capability building and knowledge diffusion through ‘on-the-job’ learned experience, leading to an uneven distribution of capabilities across regions. Indeed, the role of donors in strengthening local research capacity through international collaborations in many lower and middle-income economies has been deemed crucial, as these countries tend to lack research capacity and face problems translating research into impact. In this respect, it seems more important than ever that large research funders such as those within the EU and the US support international collaboration on a scale that far outstrips current levels. While funders, such as the US Agency for International Development’s (USAID) Partnerships for Enhanced Engagement in Research or the UK’s Newton Fund already have dedicated mechanisms to support North-South research partnerships, this could mean expansion of National Science Foundation (NSF) programmes to allow non-US research leads, or a U-turn in the recent decrease in funding allocated to the much-feted Fulbright programme (which supported two of the authors of this paper to spend time in the US). One bright spot is the recent growth of development-oriented research funding in the UK, the Global Challenges Research Fund (which supported this work), that not only supports equitable UK-developing nation collaboration but mandates it. It is only with large scale investment in such programmes that international research collaboration will continue to play a vital role in global capability building.

While previous work comparing data sources on academic publishing highlight the comparative strength of the Scopus dataset, we are aware that there are limitations to this data given our interest in comparison between countries. The database’s coverage is thought to be weaker for the social sciences and humanities, and for literatures in other languages than English (Aksnes and Sivertsen 2019). Furthermore, we cannot ascertain that the indexing of work from publishers located in countries where academia is less well resourced is as complete as for countries with more established academes. Additionally, our dataset includes journal articles, books, and conference proceedings, but no other types of academic outputs. Ideally one would complement this analysis with additional material from academic search engines such as Google Scholar, which contains up to four times as many documents as Scopus. However, due to well-known issues such as document duplication, false citations, and unstable search indexing, this would require a major investment in data cleaning and processing.

There are a few clear avenues for future work. Firstly, much remains to be understood about the nature and evolution of global collaboration networks, and the roles of individual actors. For example, it would be interesting to investigate whether we can identify global hegemons, countries playing the role of gatekeeper between lesser regional partners and the rest of the world. Similarly, our work suggests that colonial links and geopolitical alliances shaped, for a time, regional basins of knowledge. Has this transition from historical blocs to regional clusters positively (or negatively) impacted the research capacity of less developed nations? In other words, who have been the winners and losers from this shift in network structure?

Finally, there is ample scope and reason to further investigate the structure of global research ties and knowledge diffusion beyond inter-country links. First and foremost, research quality and disciplinary focus is often highly institution—rather than country—specific. Furthermore, funding programmes often target specific fields and institutions. For these reasons amongst others, it would be fruitful to dis-aggregate the global collaboration network by institution and field. Perhaps collaborations in certain disciplines are heavily demarcated along regional lines, while others flourish under international collaboration. Perhaps top-tier institutions maintain international links, while second-tier institutions focus more on regional ties. Additionally, there are a large number of possible metrics one might compare to co-authorship ties, including researcher mobility patterns. I.e, is the recent regional retrenchment in collaboration patterns also observed in researcher mobility patterns?