CHAPTER 3: WHO SHAPES PLANT BIOTECHNOLOGY IN GERMANY?
5. R ESULTS
5.1 Science and technology space analysis on the network level
104 The analysis is supported by the estimating, whether there exist differences between matched and non-matched samples. This is done by looking at the representation of matched nodes across the nodes with highest centrality indicators. Such method may show, whether matched actors are overrepresented among the most influential nodes. Furthermore, statistical test was performed in order to see, whether there are overall differences of centrality measures before the groups of matched and non-matched nodes in order to follow, whether matched author-inventors stand out from just-authors or just-author-inventors.
4.2 Text mining applications
After performing network analysis, text mining techniques are used in order to identify the main topics along the matched network as well as for co-authors’ and co-inventors’ networks separately. This allows showing the topics, which have importance only for science or only for technology as well as the ones, which are relevant for both fields. Apart from that, as the keywords for subsequent periods may differ, the analysis may also help to show, how the topics have developed over time.
As the input for the analysis patent and paper titles were taken. They provide the key idea of the scientific or technological output. Only English papers and patents are taken into account in order to avoid inaccurate translation. As the result of language filter, 1664 patent families (more than 80% of all identified families) and all papers were left. Apart from that, stemming of the dataset was performed in order to delete stop words, plurals and numbers.
On the last step, according to Silge and Robinson (2017) codes for RStudio48, the most co-occurring keywords were created both for non-matched and matched actors, and visualized based on frequencies of the co-occurrences. Thus, the picture of the field could be generated as well as separate clusters of connected keywords could be identified. By comparing the most occurring keywords along matched and non-matched nodes’ networks it could be seen, how authors, inventors and author-inventors differ regarding their research fields.
105 a Co-inventors’ network, 1995-1999 b Co-inventors’ network, 2000-2004
c Co-inventors’ network, 2005-2009 d Co-inventors’ network, 2010-2015 Fig. 3 Co-inventors’ networks over time
Figure 3 shows co-inventors’ networks for four sequential periods. As can be seen, in the end of 90s one big cluster existed along the network, followed by many smaller cliques of inventors.
Not unusual was also the situation of just two inventors working and patenting together. In the second and third period several other clusters of inventors appeared, which grew with the time, whereas the first component diminished in size. In the last period, starting from 2010, several clusters of co-inventors can be seen, without any of them strongly dominating in size. Thus, it can be seen that the general trend goes away from big teams towards smaller ones, which can also probably be translated into the trend from big towards smaller firms in plant biotechnology. Following analysis of network measures will show more insights from these networks.
Table 1 shows measures, obtained on the overall network level for the co-inventors’ network.
As could already be seen from the figure 1, the network first tends toward consolidation, with number of components getting smaller and their size getting bigger, and then towards separation with many small components appearing. This is also reflected in the diminishing average length of the path and decreased network diameter, shown in the table. In general, it
106 can be said that with regards to inventions, the industry experienced structural changes in the mid 2000s, which were followed by re-orientation and new wave of inventors coming to field in 2010s.
This statement can also be supported by the number of the nodes, remaining in the network.
It can be seen, that the proportion of nodes, remaining or reoccurring in the network, changes over time. Whereas in the second and third observation periods this number is growing, and between 2005-2009 each 4th node has been seen in the network before, in the fourth period this number falls to less than 20%. It shows, that 2010s were marked by new inventors, who either followed the changing technological trend within biotechnology and changed the field of patenting or just created the first patent.
Tab. 1 Co-inventors’ overall network measures
1995-1999 2000-2004 2005-2009 2010-2015
Number of nodes 653 833 656 872
Number of edges 1498 2100 1435 1677
Share of isolates 0.081 0.102 0.216 0.157
Degree centralization 0.068 0.062 0.053 0.043
Average length of the
path 3.911 3.843 2.874 2.268
Network diameter 10 10 9 8
Number of components 105 124 100 151
Component ratio 0.172 0.148 0.151 0.172
Size of the biggest
component 155 179 75 48
Size of the 2nd biggest
component 19 38 25 38
Size of the 3rd biggest component
14 37 23 29
New nodes - 0.813 0.780 0.859
Remaining nodes - 0.187 0.220 0.141
Reoccurring nodes - 0.187 0.252 0.197
New edges - 0.937 0.916 0.939
Remaining edges - 0.063 0.084 0.061
Reoccurring edges - 0.063 0.085 0.081
Interestingly, the number of remaining or reoccurring edges is approximately 2.5-3 times less than that of the nodes. It means, that even the nodes, remaining in the network, mostly have changed their co-inventors. This situation may especially often occur within corporations with large development teams or along big institutions, which mostly work on the projects. Notably, in the last period the number of reoccurring edges is 2% higher than the number of remaining edges. It means, that some nodes revived their co-inventorship from the beginning of 2000s.
107 a Co-authors’ network, 1995-1999 b Co-authors’ network, 2000-2004
c Co-authors’ network, 2005-2009 d Co-authors’ network, 2010-2015 Fig. 4 Co-authors’ networks over time
Next, the development of co-authors network is visualized and presented on figure 4.
Comparing to co-inventors’ network dynamics, for the case of co-authors’ increase of the size of the main component along with the general increase in number of nodes can be observed.
No other large components, comparable to the first one, appear along the network.
Thus, additional exploration is important here, which can be provided by introducing the overall network measures (table 2). Here the tendencies are different from those of the co-inventors’ networks. Namely, the main component, which composes about 2/3 of the overall number of nodes, grows over time, with number of components and isolates increasing only slightly, thus, network rather converges. It means, that large proportion of nodes, coming to the network with time, is being added to the main component, or the existing nodes are starting to collaborate with main components’ participants.
Apart from that, the share of isolates is growing, although only slightly, which means that the number of single-authored entries is increasing with time. The lower diameter and average path length as well as significantly increased number of edges show, that new ties are appearing between previously unconnected actors, which is mostly probable for the main path.
Additionally, this tendency can be seen in the large number of new edges, at least 94,4% of the
108 network edges have not occurred in network before. It means, that only small number of authors maintain their co-authorship ties. It especially contrasts with the number of nodes, remaining or reoccurring in the network, which is growing over time and is reaching in 2010-2015 more than 38%.
Tab. 2 Co-authors’ overall network measures
1995-1999 2000-2004 2005-2009 2010-2015
Number of nodes 10838 15183 16457 18777
Number of edges 53351 65578 68769 73959
Share of isolates 0.006 0.004 0.023 0.039
Degree centralization 0.017 0.012 0.011 0.009
Average length of the path
8.187 7.920 7.666 6.952
Network diameter 24 21 21 20
Number of components 613 797 964 1044
Component ratio 0.057 0.050 0.059 0.056
Size of the biggest
component 7028 10208 10328 12862
Size of the 2nd biggest component
76 98 68 69
Size of the 3rd biggest
component 55 47 67 51
New nodes - 0.823 0835 0.783
Remaining nodes - 0.177 0.165 0.217
Reoccurring nodes - 0.177 0.181 0.383
New edges - 0.985 0.999 0.946
Remaining edges - 0.015 0.001 0.054
Reoccurring edges - 0.015 0.006 0.056
Thus, the first conclusion which can be made here is that co-authors’ and co-inventors’ network have different development paths. Whereas the co-inventors’ network has a trend towards divergence and dissimilation of the main component, the network of co-authors’ experience constant growth of the main component, which takes bigger part of the network, whereas even the 2nd largest component is more than 100 times smaller. It means, that co-authors’ network is more connected with one main topic (with probably many subtopics) developing over time, whereas in co-authors’ network several almost equally important components are developing.