Analysis of scientific collaboration in Chinese psychiatry research

Of all the publications from the Chinese psychiatry field in 2003 to 2012, more than 80 % were published by two or more collaborators and the output of achievements in scientific research by way of collaboration was consistent with the total output. This suggested that most published research was collaborative. In this study, the map was composed of 5 independent sub-networks. The line value and the distance between two vertices represent collaborative strength, while thickness of the line represents the number of co-authorship papers. In this authors’ collaboration network, the researcher working in Shanghai Mental center has the highest degree centrality of 33, indicating that with 33 collaborators, he was the most key author. In a collaboration network, betweeness centrality reflects the role that the author plays within the network. Without the author with the highest betweeness centrality, the collaborative network would be disrupted. Since the researcher working in Nanjing Medical University was the author with the highest betweeness centrality, he played an instrumental role in maintaining the cohesion of the network and thus had the power to influence collaborative relationships. In a collaborative network, the closer two authors are to each other, the more easily information is communicated and the more likely the two are to collaborate. The researcher working in Shanghai Mental health center had the lowest closeness centrality, indicating that he contributed a great deal of research and was in a central position within the entire network.

Using hierarchical clustering analysis, we divided the 98 authors into 5 sub-networks. The largest sub-network included 90 nodes and 1132 lines. In this sub-network, the average path length was 2.495 and the average clustering coefficient was 3.288, indicating a clustering effect. A researcher, who is in this sub-network, worked in the Shanghai Mental Health Center and had the highest centrality degree mainly due to his research focusing on neuroimaging of patients with mental disorders. The researcher, who also worked in the Shanghai Mental Health Center, had the second highest centrality degree mainly due to his research focusing on the neuropathology of mental disorders. The researcher, with the third highest centrality degree, mainly researched treatments for schizophrenia. The second sub-network included two researchers, who both worked in the Tian shui Mental Hospital of Gansu and researched pharmacological treatments of depression. The third sub-network included the researcher from the Fourth Hospital of Linyi in Shandong and the researcher from the Linyi Mental Health Center of Shandong, and their research focused on the classification of depression. The fourth sub-network included the researcher from Anhui Medical University and the researcher from The Fourth People’s Hospital of Hefei in Anhui, and they researched the neurophysiology of mental disorders. Finally, the fifth sub-network included two researchers from Xuanwu Hospital in Beijing, where they researched neurology.

Papers with multi-institutional collaborators accounted for more than half of all papers. Universities, research institutions and hospitals were the main institutions conducting research in this field. As collaborations among institutions increased, the total output in terms of scientific publications also increased from 2003 to 2012. In this study, there were 7,672 papers from SCI which demonstrated inter-institution collaboration from 2003 to 2012. This number increased from 418 in 2003 to 587 in 2012. These papers included 8,382 institutions that appeared a total of 32,410 times. The largest collaboration in our sample involved 20 institutions. We formed a map visualizing the structure of institutions’ collaboration network in the field of psychiatry during 2003 to 2012 (Fig. 1), the abbreviations of the geographic name and the name of university were listed in Table 2. The size of the node represents centrality in collaborative network. Shandong Mental Health Center and Mental Health Center of the Second Xiangya Hospital in Central South University had the highest degree centrality and betweenness centrality, and the lowest closeness centrality (Table 3). This indicated that these institutions were highly collaborative. The distance and thickness of the line between two nodes represents collaborative strength and the number of collaborative papers, respectively. We found that Shandong Mental Health Center, Mental Health Center of the second Xiangya Hospital in Central South University and Mental Health Center of Peking University were in the center of the collaborative network and thus played an influential role in the development of psychiatry. In contrast, The eighth People’s Hospital of Zhenzhou and The Fourth People’s Hospital of Liaocheng in Shandong were on the edge of the collaborative network. Compared to that of those institutions in the center, the scientific research strength of institutions on the edge were slightly weaker. Further, collaborations among the institutions in the center reflect ‘the center of the obvious effect’ in the process of co-authorship while the institutions on the edge collaborated more loosely. Analyzing Core-Periphery was a quantitative way to study networks. We applied this method to the collaboration network and found that the ‘correlative value of collaborative network’ was 0.698 (UCINET 6.0). A well-delineated Core-Periphery structure appeared (Fig. 2), indicating that the collaborative network was regional, whereby research institutions in close geographical proximity were more likely to collaborate, the abbreviations of the geographic name and the name of university were listed in Table 2. Shandong Mental Health Center and Shandong University, with the greatest number of collaborations, had a close collaborative research relationship.

From 2003 to 2012, the most productive regions were Shanghai, Beijing and Shandong (Fig. 3), with 2,154 psychiatric research papers originated from Shanghai, making up 13.07 % of all such papers. Using the top 31 most productive regions in China to construct the map of scientific collaboration (Fig. 4), the network included 31 nodes and 226 lines. The average path length was 1.858 and the average clustering coefficient was 5.363, indicating a pronounced clustering effect. We applied Core-Periphery analysis and calculated the correlative value of the collaborative network to be 0.904 (UCINET 6.0). Once again, a well-delineated Core-Periphery structure appeared (Fig. 5).

We analyzed the effect of scientific collaboration on regions’ scientific output by correlating nodes to their corresponding scientific research achievements. For each of the 31 regions, we examined the number of ties and number of papers produced (Table 4). We found that the quantity of international collaborations correlates with research output. This suggested that international scientific collaboration greatly influenced scientific output in this field, whereby countries that collaborated frequently had a greater research output.

A centrality analysis (Table 5) revealed that Beijing, with the highest degree centrality of 120, highest betweenness centrality of 177, and lowest closeness degree of 90, was the center of the scientific collaboration network of China’s psychiatry field. Beijing’s extensive research output made it a major producer of international publications.