When it comes to networking in Biostatistics, the well-known rule of the 6 degrees of separation seems to get narrower.
Intrigued by Michael Salter-Townshend´s article in the last month´s Significance Big Data Special Issue, I tried the InMaps Linkedin application for both my profile and Biostatnet´s (with the permission of its main researchers).
At first glance, it can be noticed that there are obvious differences between the two of them, most probably due to the fact that mine includes friends and family that are not necessarily linked to the field of Biostatistics, and therefore does not show such a clear conglomerate of mutually linked connections (or small world network), rather being divided in two main clusters (forming a sort of scale-free network): one that could be identified with my social life and previous studies (dark turquoise), and the other one (rest of colours) intimately related to my current employment. It is also worth noticing that the coloured clusters in Biostatnet´s map are not necessarily associated to the nodes that constitute the network, but to the different areas of study (clinical, applied,…) instead. This clearly reflects the multidisciplinary nature of an area of study that requires of other fields such as Biology, Computing, Mathematics and Medicine for its successful development.
However, the importance of these maps does not just lie in the identification of clusters but in the potential for inferring further information from them. As a matter of fact, it has been shown that the often criticized social networks, can not only help us when bored or looking for a job, but do also encourage and make interdisciplinarity easier, and provide researchers with essential information for the study of scientific phenomena such as the spread of epidemics, since this is very often determined/affected by social interaction (see papers by Liu and Xiao and Corner et al.). This also applies to the study of the distribution of species in ecological niches whose analysis is certainly similar to that of social networks (see papers by Johnson et al and Coleing). It has been proved that those species that are involved in a trophic chain with more and better connections, will be more likely to survive should any changes in their environment happen.
In conclusion, it seems that when networking, two highly-connected contacts are already a crowd and provide much more information than we could ever imagine, so…let´s network!!
Have you tried with yours? Any surprises there? Have you used network analysis in your research? Tell us about it!!