Detecting Dynamics of Hot Topics with Alluvial Diagrams: A Timeline Visualization

Publications in the research field of scientometrics were chosen for this study. Data was retrieved from Thomson Reuters’s (presently Clarivate Analytics) Web of Science (WoS) on May 16, 2016. To collect the most relevant publications in the field of scientometrics during the past five years, we conducted the literature retrieval in the following steps. First, we searched for articles published in Scientometrics since it was founded in 1978, and retrieved a total of 3,901 publications. Second, we collected all data that had cited at least one of these 3,901 articles and were published in the period of 2011–2015. Third, these citing articles were restricted to three WoS categories: (1) Information Science & Library Science, (2) Computer Science, Interdisciplinary Applications, and (3) Computer Science, Information Systems. The eligible articles were combined with 1,397 articles that were originally published in Scientometrics between 2011 and 2015, and finally we obtained a total of 3,368 articles in the field of scientometrics.

There are many popular timeline designs to fit different information streams, such as the three-dimensional spiral timeline, chessboard timeline, interaction timeline, relationship timeline, Gantt timeline, and complex timeline. Choosing an appropriate timeline approach depends on user demand. We used the alluvial diagram to demonstrate the dynamics of hot topics and to identify the structural changes of research. Specifically, main clusters in a scientific network at a given time occupy a column in the diagram and are horizontally connected to significant preceding and succeeding clusters by stream fields. We generated a co-word network before importing and generating the alluvial diagram. We then clustered the network and gave a label to each cluster. This approach relies on word profiles derived from articles citing a cluster of co-word articles, based on the assumption that the word profiles characterize the nature of a co-word cluster.

In the alluvial diagram, PageRank is used to reflect the importance of each cluster and word. We not only consider the frequency of keywords, but each keyword’s weight using PageRank (Page et al., 1998). PageRank is a link-analysis algorithm which is designed and used by Google to measure the importance of a webpage in the first place. After years of development, this algorithm has been used everywhere in network analysis. Using PageRank, we are able to identify hot topics more accurately than the traditional approach that only considers keyword frequency. PageRank gives each keyword a value of weight. The words used by a paper with high influence will be set a higher weight than those used by an ordinary paper. This idea was actually initiated from citation analysis (Pinski & Narin, 1976), which posits that the number of citations a paper receives can reflect the influence and importance of its research. PageRank algorithm extends this approach by not counting inbound links of all pages equally, but normalizing the number of outbound links and importance of neighboring pages, as shown in Equation (1): PR(p)=(1−<!-- - -->d)+d∑<!-- ? -->i=1nPR(Ti)C(Ti), $$ \displaystyle PR(p)=(1-d)+d\sum\nolimits_{i=1}^{n}\frac{PR(T_i)}{C(T_i)}, $$ (1)

where PR(p) means the PageRank value of page p; T_i (i = 1, 2 …) means the inbound links of page p; C(T_i) denotes the number of outbound links on page T_i; PR(T_i)/C(T_i) means the PageRank value that page T_i (the inbound link of page p) gives to page p; d is the damping factor which can be set between 0 and 1 and reflects the probability that a user reaches a page randomly; and (1 − d) gives the total PageRank value of all pages as 1. In this study, we set d equal to 0.85.

We first calculated the frequency of keywords and chose the top 100 keywords with frequencies beyond 48. There were 28 significant keywords that needed to be normalized. Concentrating on hyphenated words and singular and plural nouns, we then replaced these keywords with normalized keywords in the original data set. This provided unitive data for further analysis.

After choosing the top 5% of frequent keywords from the articles, we generated five co-word networks with CiteSpace for years 2011 through 2015. By conducting cluster analysis, we detected hot topics for each year. After that, we drew an alluvial diagram between years, which displayed the dynamics of every cluster. In the alluvial diagram as shown in Figure 1, each block represents a cluster. The height of a block represents the cluster’s PageRank value. Blocks were ranked in descending order by PageRank value from the bottom to the top. To do this in a simple way, only the dynamics of the top 13 clusters with the highest PageRank value was shown, such as the cluster of Industry, Google Scholar, and Italy.

With the alluvial diagram in Figure 1, we can intuitively observe the mergers and splits of the top clusters from 2011 to 2015. The height of a stream field represents the flowing nodes’ total PageRank value. From this diagram, scientometrics has undergone constant development and changes in recent years. The Industry cluster had the highest PageRank value in 2011 and also the highest PageRank value in the whole five-year period. The main research contents of the Industry cluster is study of network collaboration among authors, organizations, or nations using scientometric methods, where both the characteristics and influencing factors of collaboration can be identified. This research aims to promote collaboration and drive knowledge flow between industries and universities, and help researchers anticipate and address the demands of industry to develop new technologies. From this hot topic of Industry, we are able to confirm that scientometric research has played a significant role in scientific and technological development by assisting in the decision-making issues in recent years. This promotes the research focus on the industry-related research using scientometric methods. Obviously, application-oriented research is at the center of scientometric research, where its scope is still constantly expanding.

To analyze the importance of every cluster, we listed the name of every cluster and its PageRank value from 2011 to 2015 in Table 1.

Table 1

PageRank values of the clusters from 2011 to 2015.

As seen in Figure 1 and Table 1, we found that the cluster Industry and Triple helix had high PageRank values every year from 2011 to 2014. Triple helix refers to the three-way, innovative government-industry-university cooperation in this field, originally based on the “three-screw” structure in biology. It implies that research related to industry and government-industry-university collaboration was a hot topic in scientometrics during these years. Furthermore, there were some emerging clusters that showed up in 2015, such as Sex differences and Self-organization. The cluster of Sex differences was an interesting topic because researchers of different sexes had different performances in some scientific fields. Another cluster of Self-organization belongs to the scope of complex networks with big data backgrounds. Along with the rapid development of information science, research about complex networks will attract more attention, even in the field of scientometrics, because information science can promote its development.

The dynamics of the Industry cluster, which has the highest PageRank value over the five years, are marked in red in Figure 2, in which all of its topic splits and mergers are presented.

The Industry cluster split into four clusters in 2012 and maintained this status for the next three years only. The reason for this is the cluster was labeled by the keyword within it with the highest PageRank value. The research topics of Industry in 2012 became more intensive than in 2011. First, the Patent citations cluster in 2012 analyzed the state of technology development based on patent citations. Second, the Triple helix cluster in 2014 investigated collaboration among government, industry, and universities, where it became one of the main inflow clusters of Industry. Third, the Diffusion cluster in 2012 mainly refers to knowledge diffusion on the networks, such as institutional collaboration networks and national collaboration networks. Last, the Centrality cluster in 2012 was an important measure in the social network analysis. It had significant implications for identifying the author or institution that occupied the core place of the network. Based on this change, the research contents of the Industry cluster became more specific. By analyzing its dynamic changes from 2012 to 2015, we found the steady cluster Innovation, which split off from the Industry cluster. This finding is in accordance with the current demands placed on technology development and reflects researchers’ attention paid to innovation.

In order to see the details of dynamic changes of the Industry cluster, we listed the node of every cluster and highlighted its nodes in blue in Figure 3. The keywords of every cluster were listed in descending order based on its PageRank value.

.If the keyword is used frequently in 2011 as well as 2012, it will show up in the diagram. Every node represents a dynamic change, and there are no keywords with a high PageRank value during all five years. Although most keywords do show up in the following year, they do not have equal importance. In Figure 3, the keyword industry did exist in a cluster in 2012, but its PageRank value was lower than that of the keyword diffusion. Of the top three keywords with a high PageRank value in 2011, industry, triple helix, and innovation, the latter two were also significant in the following four years, even exceeding industry—to become the top two hottest research topics in 2015. In 2014 Innovation became the most significant cluster with the highest PageRank value, and in 2015 it split into four keywords from the Industry cluster and came to occupy the top four positions. This suggests that research on innovation was a hot topic in 2015, yet its research content was a little different from 2011. The topic interdisciplinary also belongs to this Innovation cluster, reflecting that interdisciplinary study became a new and hot research topic this year. The three keywords, knowledge, collaboration, and dynamics, flowed into three different clusters in 2015 (Figure 3) and were ranked in a low position in their respective cluster, where they did not cluster with other keywords. This reflects their weak relationship with other keywords, in that they could not represent the main research content of their respective cluster. The Innovation cluster thus became the final, significant cluster of the Industry cluster, which drew attention of many researchers in 2015.