Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019

The collaboration between authors can intuitively reflect the degree of collaboration in the paper. Papers written by only one author are called single-author papers; papers written by two or more authors are called collaborative papers. Collaboration at the author level was measured by the number of authors of the paper. Figure 3 shows the total number of papers, single-author papers, two-author papers, three-author papers, and four-or-more author papers in the filed of AI from 1985 to 2019. The trend line chart shows the percentage of each type in the total number of papers.

Figure 3

During these 35 years, 84.8% (32,412 papers) of papers in AI were collaborative, while only 15.2% (5,812 papers) were created by a single author. Two-author papers, three-author papers and four-or-more author papers accounted for 24.9% (9,509 papers), 23.4% (8,960 papers) and 36.5% (13,943 papers), respectively. This indicates that the collaboration in the field of AI has reached a certain level of maturity. The percentage of single-author papers showed a declining trend. The percentage of single-author papers in 1986 was the highest of all paper types, at 46.2% (18 papers). In 2019, the share of single-author papers fell to 11.4% (607 papers), the lowest of any paper type. In fact, since 2003, the percentage of single-author papers and its absolute number have been the lowest of all paper types, reflecting the increasing degree of scientific collaboration in the field of artificial intelligence. The proportion of two-author papers rose rapidly from 1985 to 1991 and was the dominant pattern of collaboration papers from 1989 to 2008. After 1991, it showed a downward trend year by year. Especially after 2012, two-author papers have become the one with the lowest proportion of all collaborative papers. Similarly, before 2013, the proportion of papers of three authors generally showed a slow rise, after which the proportion of papers of this type of collaboration began to decline, with the post-2016 timeline showing a steep decline (26.5%, or 617 papers). The proportion of papers with four or more authors in 2016 also showed a significant shift. From 2013 to 2016, the proportion of papers of this type decreased slightly, while from 2016 to 2019, the proportion of this type increased strongly. In 2019, the proportion of papers of four or more authors even exceeded half of all papers, reaching 54.4% (2,894 papers). Moreover, since 2009, in both proportion and absolute number of papers, papers with four or more authors were the most of all paper types, and in this period this paper type was the only one to show an increase over time.

Scientific research in the field of AI is mainly carried out in the form of collaboration, and as time goes by, the number of authors and degree of collaboration have been increasing. From 1989 to 2008, the two-author paper was the main pattern for the collaborative paper, while after 2008, the four-or-more author paper became the mainstream form of the international collaborative paper in the filed of AI. During the past 35 years, the proportion of papers with single authors gradually decreased. And although the proportion and number of papers jointly written by two or three authors were more than that of single-author papers, the timeline also showed a declining trend. It can be inferred that the proportion of papers with fewer than four co-authors will continue to decrease, while those with more than three co-authors will continue to increase. Papers with four or more authors were the only type of collaborative papers which showed an increase, and papers with four or more authors accounted for more than 50% in 2019. Therefore, it can be judged that the form of collaboration with more than three authors is the main pattern of collaboration in the field of AI, and the scientific collaboration in this field presents a trend of increasingly many authors.

Collaboration at the author level reflected the main collaboration patterns in the filed of AI, as well as the evolution of the degree of collaboration based on the number of authors. Due to a lack of national information, however, international collaboration is not reflected. In this section, we explore collaboration trends and major patterns at the country/regional level. Similar to collaboration at the author level, we define papers with only one country/region name in the C1 field as single-country papers, and we define papers with two countries/regions as two-country papers, and so on. In addition, if the authors of the paper are all from the same country/region, differences between the collaboration paper and the single-author paper cannot be reflected. Therefore, we define the papers as domestic collaboration papers if their co-authors are all from the same country/region. In contrast, paper co-authors from two or more different countries are defined as international collaborative papers. Figure 4 is the trend line chart of international collaboration in the field of artificial intelligence at the country/regional level from 1985 to 2019.

Figure 4

Over these 35 years, the number of single-country papers (31,499 papers) and the percentage of them (82.4%) are significantly higher than that of any other types. In 1986 and 1988, the proportion of single-country papers even reached 100%, that is, all papers in the field of artificial intelligence were single-author papers or domestic collaborative papers. Moreover, the proportion of domestic collaborative papers (67.2%, or 25,687 papers) is 49.6% higher than that of international collaboration papers (17.6%, or 6,725 papers). This means in the field of AI, single-country papers and domestic collaborative papers are the main types of international collaborative papers, indicating that scientific collaboration between researchers from the same country is the main pattern of collaboration in the field of AI. Although the number of single-country papers and domestic collaborative papers accounted for the majority, with the passage of time, the proportion of such papers gradually decreased, showing a general downward trend. In 2019, the proportion of single-country papers dropped to 72.2%, and domestic collaboration papers accounted for 60.7%. The proportion of multi-country collaborative papers has been increasing, however, which shows the research in the field of AI is gradually shifting towards multinational collaboration.

For multi-country collaborative papers, the number of two-country papers remained in the lead except for in 1987. The proportion of two-country papers for all international collaborative papers from 1985 to 2019 is 77.2% (5,192 papers), indicating that two-country collaboration (Hu, Wang, & Huang, 2019) is still the main pattern in global artificial intelligence collaboration. In Figure 4, we notice that from 1985 to 2009, the trend lines for two-country papers and international collaborative papers basically overlap, which means that international collaboration was almost carried out in the form of cooperation between two countries before 2009. Although the proportion of international collaborative papers in 35 years is not high (17.6%, or 6,725 papers), it has increased from 0% in 1986 to 27.8% in 2019. This shows that scientific research collaboration in the field of artificial intelligence has developed towards internationalization.

Research in the field of AI at the country level revealed the main patterns and development trends of international collaboration from a macro perspective, but the granularity was not sufficiently fine. For example, for single-country papers, it is impossible to distinguish between single-author papers and multi-author papers from the same country. Also, international collaborative papers only showed the number of countries participating in collaboration, but no more detailed information can be obtained. Therefore, this section takes the institution as the minimum countable object and further investigats collaboration at the institutional level. In addition, country information was included in the statistical process to present a more granular picture of international collaboration. Figure 5 shows the time distribution of single-institution papers, two-institution papers, three-institution papers, and four-or-more institution papers, and it also shows the proportion of domestic-institution collaborative papers, international-institution collaborative papers, and international papers with two institutions, over time.

Figure 5

Similar to country-level collaboration, the proportion of single-institution papers in total papers was 51.8% (19,791 papers), the highest of any type of paper at the institutional level. This suggests that more than half of the research in the field of AI over the past 35 years has been done independently within a single institution. In the single-institution papers, 70.6% (13,979 papers) were co-authored, while only 29.4% (5,812 papers) were written by a single author. This further indicates that intra-institution collaboration has been the main pattern of collaboration research in the field of AI. The proportion of single-institution papers showed a downward trend, with the highest proportion (84.3%, or 43 papers) in 1990 and a drop of over 50% to 32.0% (1,701 papers) in 2019. This shows that multi-institution collaborative papers are gradually becoming the mainstream.

The share of two-institution papers was on the rise, from 17.1% (7 papers) in 1985 to a peak of 30.3% (1,232 papers) in 2017. But after 2017, the share of two-institution papers declined for two consecutive years to 24.6% (1,309 papers) in 2019. By contrast, though the proportions of three-institution papers and four-or-more institution papers were lower than that of two-institution papers before 2019, they maintained an upward trend over the past five years. The proportion of papers with four or more institutions showed a high growth rate in particular. In 2019, they exceeded the proportion of two-institution papers and three-institutions papers. And over the past 35 years, the number of two-institution papers (25.2%, or 9,620 papers) was 2.0 times that of three-institution papers (12.4%, or 4,738 papers) and 2.4 times that of four-or-more institution papers (10.7%, or 4,075 papers). This indicates that the two-institution paper has been the main pattern of collaboration in the filed of AI at the institutional level. In addition, this shows a trend of multi-institution collaboration, and the proportion of papers with more than two institutions is increasing.

The proportion of domestic collaborative papers and international collaborative papers shows an increasing trend, indicating that the degree of collaboration in the field of AI is deepening. Between 1985 and 2019, 30.6% (11,708 papers) were domestic collaborative papers, and 17.6% (6,725 papers) were international collaborative papers. This result shows that domestic institution collaboration is the main pattern of international AI collaboration at the institutional level. Two-international-institution papers produced 44.1% (2,963 papers) of collaborative papers, while three-international-institution papers produced 21.8% (1,463 papers). This showed that international papers with two institutions have been the main pattern of collaborative papers between international institutions. In Figure 5, it can also be seen that the proportion of international papers with two institutions also began to decline from 2017, and the overall trend over time trend was similar to that of two-institution papers. This indicated that the proportion of collaborative papers with more than two institutions has also been increasing.

In this section, we analyzed international collaboration in the filed of AI from a macro level. Figure 6 shows a country collaboration network built using Pajek software. Each node in the graph represents a country/region. The text next to the node is used to explain the name of the country/region represented by the node. The value in brackets is the degree of the node. The larger the degree of the node is, the larger the circle area of the node is, and the nodes of the same degree share the same color. An edge between nodes indicates collaboration between two countries/regions. Over the past 35 years, a total of 65.7 percent of countries (153 of 233 countries/regions) have participated in international collaboration in the field of AI. International participation, however, in neurosurgical (Frey et al., 2017) was 28.8% (67 country/region) and 33.0% (77 country/region) in brain death (Doğan & Kayır, 2019). This reflects that the international collaboration in the field of AI has been widely involved with other fields, with a high degree of collaboration. It should be noted that the country collaboration network contains 1,948 edges, but only 849 of them have a weight greater than two; that is, the two countries connected by such edges have only cooperated once or twice, and the collaboration between such countries is not close. To highlight the core and clearly show the network structure, we deleted all edges with weights less than five, and we deleted isolated nodes. Also we did not reflect the edge weight information in the image. The resulting network consists of 78 nodes and 586 edges. The nodes with large degrees are located in the central region of the network, while the nodes with small degrees are distributed in the edges of the network.

Figure 6

It can be clearly seen from Figure 6 that the countries/regions represented by the nodes with the largest degrees are the US, the UK, France, Spain, China, and Germany. The larger the degrees are, the more nodes are directly connected to this node in the network, indicating that the countries represented by this node have cooperative relations with more countries. The US and the UK were at the core of the network, conducting collaborative research in the field of AI with many countries/regions around the world. After calculation, the average degree of the whole network is 25.46, indicating that each country/region has an average of 25–26 partners. On the other hand, the average degree of the country collaboration network was 10.70 between 1985–2017 (Hu, Wang, & Huang, 2019). This shows that after 35 years of development, international collaboration in the field of AI has reached a high level of maturity and has been especially deepened in the last two years.

Betweenness centralization (BC) is another measure of a node's ability to control the network. In collaboration networks, a small node may play a key role in connecting the whole network. After calculation, the BC of the whole network is 0.15665359, and the BC of the adjusted network is 0.18289824, as shown in Figure 6. Table 2 lists the top 20 countries/regions in terms of BC.

As can be seen from Table 2, the countries with high BC are the US, Spain, France and the UK. In the country collaboration network (Figure 6), these countries also have high degree. This shows that they played a key role in international collaboration in the field of AI between 1985 and 2019. They did not only have a wide range of overseas partners, but also played a core and leading role in artificial intelligence research. We found that China and Germany, however, also had high degrees, but were slightly lower in the BC ranking. On the other hand, Malaysia, Slovenia and even Serbia with degree values of 3 had relatively high BC values. These countries also played an important role in connecting the entire network of international collaboration. This phenomenon indicates that more partners do not mean a stronger ability to control the collaboration network. It also reflects the problem that the degree to which a country/region participates in international collaboration cannot be fully reflected only by the degree and BC of nodes. Therefore, we adopt a normalization method to eliminate the dimensional differences of different countries’ publications, to further analyze the degree of participation of countries in international collaboration. We calculate the ratio of the number of international collaborative papers to the total number of papers in each country, listing the countries/regions with a total publication volume of more than 100 papers from 1985 to 2019, as detailed in Table 3.

In Table 3, the top five countries are Vietnam, Saudi Arabia, the United Arab Emirates, Pakistan, and Sweden. Although these countries are on the periphery of the country collaboration network, their international collaboration publications account for a considerable proportion of all publications. For Vietnam in particular, nearly 90% of the papers in the field of AI are international collaborative papers. This shows the high level of participation of these countries in international collaboration. The US, the UK, France, and Spain, however, which rank highly in terms of the degree of nodes and BC, had a relatively low proportion of international collaborative papers. In particular, the in US, which is at the center of the country collaboration network, only 57.14% of its papers are international collaborative papers.

In this section, we analyzed international collaboration in the filed of AI from the micro level. The smallest unit used to build the network is the institution. Similar to the bibliometrics analysis at the institutional level, we construct a collaboration network from the perspectives of all institutional collaboration and international institutional collaboration, respectively. The international institution collaboration network only considers institutional collaboration from different countries. On the basis of the former, the complete institutional collaboration network adds institutional collaboration from the same country/region.

3.2.2.1 Complete institutional collaboration network

Between 1985 and 2019, 15,351 institutions around the world participated in scientific collaboration in the filed of AI. There were 54,565 edges, however, with weights of only 1, while there were 7,306 edges with weights greater than 1. Similarly, edges with weights less than eight and isolated nodes were removed to facilitate the presentation of the network structure. In the end, the complete institutional collaboration network consists of 128 nodes, 121 edges and a total of 29 subnets. The largest subnet contains 48 nodes, while the other two large subnets contain 14 and six nodes, respectively. There is one subnet that contains four nodes, and there are six subnets that contain three nodes. The rest are composed of two nodes. The three subnets containing the largest number of nodes are shown in Figure 7 below. For convenience of distinction, these three networks are called sub-network 1 (SN-1), sub-network 2(SN-2) and sub-network 3(SN-3), respectively.

Figure 7

The average degree of the complete institutional collaboration network is 8.06, indicating that over the past 35 years, on average, each institution cooperated with eight other institutions in the filed of AI. The network structure of the entire SN-1 is two closely related network groups connected by Xi’an University of Science & Technology (China). One of them takes Chinese Academy of Sciences (CAS) (China) as its center of scientific collaboration, and the other takes Islamic Azad University (Iran), Duy Tan University (Vietnam), the University of Kurdistan (Iran), and Ton Duc Thang University (Vietnam) as their collaboration center. We find that almost all the nodes that have cooperative relations with the CAS come from China. Similarly, seven of the 10 institutions that have worked with Islamic Azad Univ are from Iran. Four of the eight institutions in partnership with Duy Tan University are from Vietnam. In addition, in the other two subnets, almost all of the institutions that have partnerships with the central institutions are from the same country. The core institution of SN-2 is MIT, and most of the institutions are from the US. The core institution of SN-3 is Germany's Cancer Research Center, and most of the institutions in that network are from Germany. Unlike the other two subnets, SN-3's institutions are all medical.

The complete institutional collaboration network is not a fully connected network but consists, instead, of several sub-networks, indicating that inter-institution collaboration has not yet reached the maturity of country collaboration. Each subnetwork reflects that collaboration at the institutional level is strongly featured by regional agglomeration. Collaboration is more likely to occur between geographically adjacent institutions. Collaboration between domestic institutions tends to be deeper, while collaboration between international institutions appears more fragile. At the institutional level, the core groups in the field of artificial intelligence research include the Chinese institution group led by the CAS, the Iran—Vietnam institution group with Islamic Azad University and Duy Tan University as the core, the US institution group with MIT as the core, and the German medical institution group with Germany's Cancer Research Center as the core.

3.2.2.2 International institution collaboration network

The international-institution collaboration network is a further refinement of the country collaboration network. It only considers inter-institution collaboration from different countries, so it better reflects the central institutions and core groups of international collaboration in the field of AI. A total of 7,730 institutions participated in international collaboration between 1985 and 2019. The collaboration network of these institutions contains 32,066 edges, but only 3,131 have weights greater than one. To visually show the situation of international collaboration at the institutional level, we removed the edges with weights less than five and isolated nodes. The resulting international-institution collaboration network has 122 nodes and 130 edges. Like complete institutional collaboration network, the international-institution collaboration network is not a fully connected network but consists of 27 subnets. The largest subnet, however, contains 65 nodes, accounting for 53.3% of the total network. The number of nodes contained in other subnets is less than five, and 22 subnetworks consist of only two nodes. Therefore, we only show the largest subnet in the international collaboration network in Figure 8 and call it the biggest subnetwork of the international-institution collaboration network (BSIN).

Figure 8

From 1985 to 2019, the average degree of the international-institution collaboration network is 8.30, which is close to the average degree of complete institutional collaboration network in the previous section. This indicates that there are on average eight overseas partners for each institution and that international collaboration at the institution level also reached a certain level of maturity in the field of artificial intelligence. In Figure 8, the nodes with larger degrees are Ton Duc Thang University (Vietnam), the University of Kurdistan (Iran), Duy Tan University (Vietnam), and Islamic Azad University (Iran). These four institutions cooperate stably with more than 10 international institutions and are the main force for international collaboration in the field of artificial intelligence. In addition, the institutions above-mentioned are all from Vietnam and Iran. Combined with Figure 7 and Table 3, it can be further concluded that Vietnam and Iran are important participants in international collaboration, which also confirms the conclusion of collaboration at the country level (Table 3); that is, the degree of international collaboration between these two countries is high. For countries such as the US, the UK, France, and Spain, which are at the core of the country collaboration network (Figure 6), their institutions are not at the core of the international-institution collaboration network. Moreover, the core institutions in the complete institutional collaboration network (Figure 7), such as the CAS (China) and MIT (US), also lie in a marginal position of the international-institution collaboration network. This finding shows that although countries that are at the center of the country collaboration network have produced a large number of papers, institutions from these countries are more inclined to establish broad and stable cooperative relationships with their own domestic institutions. Their cooperation is relatively scattered, and the degree of close cooperation is relatively low.

Figure 9 shows countries of 65 institutions in the BSIN network. The US, Iran, China, and Vietnam are among the top four institutions participating in international collaboration. Iran and Vietnam are not only at the center of the international-institution collaboration network, but they also have a large number of institutions in the network, which once again shows that they are indispensable to international collaboration in the field of AI. Although institutions from the US and China are not located in the center of the BSIN network, they still play a major role in international collaboration by virtue of their large number of institutions participating in international collaboration and their huge amount of publications (Table 3). The collaboration between international institutions also shows obvious regional characteristics, and countries located on the same continent are more inclined to carry out international collaboration. Also, international collaboration has economic characteristics, as it is easier for countries that are developed or developing to carry out collaboration. Based on the BSIN network and Figure 9, we can see two core groups in international collaboration in the field of artificial intelligence. The first is the Asian Institution Collaboration Group (Developing Countries Institution Collaboration Group) led by Iran (9), China (7) and Vietnam (6), and the other is the North American-UK Institution Collaboration Group (Developed Countries Institution Collaboration Group) led by the US (9), Canada (3), and the UK (2).

Figure 9

Table 4

Institutions with high BC in the biggest sub-network of international-institution collaboration network.

Rank	Institution	Country	BC	Degree
1	Chinese Academy of Sciences	China	0.405336672	4
2	Ton Duc Thang University	Vietnam	0.384635685	18
3	Georgia Institute of Technology	USA	0.371527778	3
4	University of Alberta	Canada	0.350694444	4
5	University of Kurdistan	Iran	0.295375309	14
6	Islamic Azad University	Iran	0.288076771	11
7	Duy Tan University	Vietnam	0.223353933	13
8	MIT	USA	0.203373016	4
9	University of Tabriz	Iran	0.153155313	7
10	Oxford	UK	0.12202381	5
11	University of Technology Malaysia	Malaysia	0.095804022	5
12	Penn State University	USA	0.091765873	3
13	University of Tehran	Iran	0.039564365	3
14	University of Technology Sydney	Australia	0.036280213	5
15	Near East University	Turkey	0.033982326	3
16	Sejong University	South Korea	0.032871873	5
17	University of Malaya	Malaysia	0.031909121	3
18	GTEV ReX Ltd	UK	0.03125	2
19	University of Toronto	Canada	0.03125	2
20	Hong Kong Polytechnic University	China	0.03125	3
21	National University of Singapore	Singapore	0.030505952	4
22	Johns Hopkins University	USA	0.030505952	4
23	University of Southern Queensland	Australia	0.028754488	4
24	University of Salamanca	Spain	0.015376984	3
25	Osaka Institute of Technology	Japan	0.015376984	3
26	Korea Institute of Geoscience & Mineral Resources KIGAM	South Korea	0.006101012	4
27	Korea University of Science & Technology	South Korea	0.005030213	3
28	University of South Eastern Norway	Norway	0.005030213	2
29	University College of Southeast Norway	Norway	0.003571429	2
30	UTM	Malaysia	0.003515133	4
31	Xi’an University of Science & Technology	China	0.001070799	3
32	Nanjing Normal University	China	0.001070799	2
33	Hanoi University	Vietnam	0.000907137	2
34	Obuda University	Hungary	0.000496032	3

As with collaboration at the country/region level, in addition to judging the importance of institutions in international collaboration according to the degree of nodes, we also calculate the BC of the network. The total BC of the international institution collaboration network is 0.03549168, which indicates that most institutions do not have an intermediary function. The BC adjusted according to the principles above is 0.10567512. The BSIN shown in Figure 8 is 0.35853266. Although the degree of the CAS (China) node is only four, its BC is the largest in the BSIN network, and it is an important node connecting the collaboration groups of Asian institutions and the collaboration group of North American—UK, playing an important role as a bridge. Similarly, institutions such as the Georgia Institute of Technology (US) and the University of Alberta (Canada) also have low degrees and high BC values. Ton Duc Thang University (Vietnam) not only has the highest degree in the BSIN network, it also ranks second in BC, which indicates that this institution is not only at the core of international collaboration at the institutional level, but it also plays an important role in maintaining collaboration in the field of artificial intelligence in Asia and even the world.