Applying clustering coefficient to the pattern of international author collaboration in neuroimmunology and neuroinflammation

Data sources

We programed Microsoft Excel VBA modules to extract abstracts and their corresponding co-author names as well as author-defined keywords for each article on January 22, 2018, from Medline since 1982. Only those abstracts published by the keyword (neuroimmunology [Title] OR neuroinflammation [Title]) were included. Others like those labeled with Published Erratum, Editorial or without author nation name were excluded from this study. A total of 2885 eligible abstracts were obtained from the Medline.

Data arrangement to fit SNA requirement

Before visualizing our results using SNA, we organized data in compliance with the format and guidelines defined by Pajek software^[15]. Microsoft Excel VBA was used to deal with data fitting to the SNA requirement.

Graphical representations to report

Statistical tools and data analyses

Google Maps^[16] and SNA Pajek software^[15] were used to display and visualized representations for eminent authors and keywords in relation with neuroimmunology and neuroinflammation. Author-made Excel VBA modules were applied to organize the data. CC represents the density of a network and a significant level (> 1.96) is defined by t-value as the formula [= CC × [(n-1)/(1-CC²)]^1/2], where n = the number of nodes in a network.

In contrast, E-I index is defined by the formula, where EL = the number of external friendship links and IL = the number of internal friendship links^[17]. The negative E-I index means a coherence cluster in existence. Similarly, the higher CC indicates many members are members’ friends linked to others. Density denotes as the ratio of the linkage members over all possible members.

Author nations and their relations

A total of 2799 eligible papers with complete author nations based on journal article since 1982 are in Table 1. We can see that the most number of articles are from the countries of USA (902, 32.23%) and China (363, 12.97%). The trend in the number of publications with authorship is present in the column of growth in Table 1. All nations but the Netherlands and Israel showed a positively increase.

The diagram in Figure 1 displays author collaboration among nations. Overall, the highest production in countries are from the USA, China, and Europe [Figure 1]. Any collaborated with others are shown with a blue line. Interested authors are recommended to click the bubble of interest to see details on a website at the reference^[18].

Figure 1. Google Maps on the topic of author collaboration in neuroimmunology and neuroinflammation (cluster coefficient = 0.61)

The eminent authors in surgery

The most prominent journals and authors with the most number of papers in neuroimmunology and neuroinflammation are J Neuroinflammation and PLoS One as well as Michael T. Heneka (Germany) and Richard M. Ransohoff (USA) [Table 2 and Figure 2]. The link on website appears at http://www.healthup.org.tw/gps/Neuroimmunologyauthor.htm^[19].

Figure 2. Google Maps on eminent authors in neuroimmunology and neuroinflammation (cluster coefficient = 1.0)

Keywords to present the research domain

The most linked keywords are interleukin (IL), IL-1beta, and blood-brain barrier [Figure 3]^[20]. We can see that the keywords consisting of many clusters with different cluster coefficients.

Figure 3. Google Maps on author-defined keywords in neuroimmunology and neuroinflammation (cluster coefficient = 0.60)

Cluster coefficients in a network

Each cluster has its cluster coefficient representing the density of a network. We found that author clusters earn higher CC than have nation clusters. Cluster coefficient has a significant effect in comparison with a considerable t-value (> 2.0), indicating author network with more significance than those of the nations [Table 3].

What this adds to what was known

Many previous types of research^[9-11] have investigated co-author collaboration using SNA. The results (the most number of articles from the USA and Europe) are similar to the findings that dominant nations in science come from the USA and Europe^[21-23], but China recently has an increasing trend in publication. Referring to the apocryphal story told to discover the co-occurrence about beer and diaper sales^[6-8], we presented a novel method incorporating SNA with Google Maps to explore the data. It can be seen that visual representations offered to the readers are rare in literature. Traditionally, it is hard to observe the association of two or more symptoms or entities together that appeared in a network at that moment.

Journal authorship collaboration compared with each other using Google Maps is illustrated in this study. We can see that many links are connecting two nations which indicate a collaboration pattern in paper publication similar to the previous study^[7]. Hence, it is easy to observe the phenomena of international author collaboration in neuroimmunology and neuroinflammation, which is inconsistent with the earlier reviews that investigated scientific collaboration of Iranian Psychology and Psychiatry Researchers^[23,24].

There are 319 papers with the keyword of SNA when searching Medline on December 21, 2017. There were only two papers^[25,26] that incorporated medical subject heading into SNA to release relevant knowledge to readers. However, only a few^[27,28] include Google Maps link like we used in our current study. The CCs we illustrated at references^[17-20] are called overall CCs. The CCs in figures are 1.0, 0.61, and 0.60, respectively, which is different from the global CCs or individual CC defined by each cluster or by each node. Evidence suggests that in most real-world networks, nodes tend to create a tightly knit groups characterized by a relatively high density of ties; this likelihood tends to be higher than the average probability of a link randomly established between two nodes^[15,29].

What it implies and what should be changed?

Scientific publication is one of the objective measurements to evaluate the achievements of a medical specialty or discipline^[30]. It is worth using SNA and Google Maps to explore knowledge to readers in the future.

Many algorithms and measures (or indicators) have been developed in SNA to the graphical exploration of our data. If we investigate any author or paper most fits the research domain in a target journal, the centrality measures can be used^[9]. It means that the core subject can be analyzed using the centrality measure^[11,24] yielded by SNA.

Strengths of this study

The way we incorporated SNA with Google Maps is unique when compared with these published papers^[9-11] merely using a single method of SNA. Another strength and feature of this study is that with Google Maps linked to the references^[18-20], for interested readers, one can manipulate the link in their own ways on the Google dashboards. The national distribution in Figure 1 allow the reader to easily understand the feature of international author collaborations in neuroimmunology and neuroinflammation. One picture is worth ten thousand words. We hope following studies can report other kinds of information using Google API in the future.

Limitations and future study

The interpretation and generalization of the conclusions of this research should be carried out with caution. First, the data from this study are from Medline for a single journal. It is worth noting that any attempt to generalize the findings of this study should be cautious in the fields of journal domains.

Secondly, although the data were extracted from Medline and carefully dealt with during every linkage as correctly as possible, the original downloaded text file might have some errors in symbols such as period and comma in author address that may lead to some bias in the resulting nation distribution.

Thirdly, there are many algorithms used for SNA. We merely applied separation components showing in figures. Any changes made along the algorithm will cause different pattern.

Fourth, the social network analysis is not subject to the Pajeck software we used in this study. Others such as Ucinet^[31] and Gephi^[32] are suggested to readers for use in the future.

In conclusion, social network analysis provides wide and deep insight into the relationships among nations in coauthor collaboration. The results can help readers in future submission to a journal in neuroimmunology and neuroinflammation.