Two is a crowd

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!!

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16 thoughts on “Two is a crowd

    • Thank you so much for the link, there seems to be an endless range of applications for these techniques. It´s my intention to further study the field and I hope to be giving in this blog new insights on the topic some time soon.

      All the best!

  1. Very interesting post!
    One of the most intriguing result related to the scale-free topologies is the way to enhance the robustness of an existing
    network: just adding (randomly) new links to it. The most number of friends (in general) we have, the most solid will be our (specific) network.

  2. Thank you for your comment!

    I was actually reading yesterday about the evolution of these networks over time, and Samuel Arbesman states in his post:

    http://www.wired.com/wiredscience/2012/06/social-networks-over-time-and-the-invariants-of-interaction/

    (based on his paper published in PLoS ONE, “Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans”.)

    that the more contacts you have the less close, therefore I would assume there would be a decrease in robustness, wouldn´t there?

    I´d really appreciate your feedback on this, as I might be misunderstanding the concept of robustness here.

    • In general, scale-free networks display a huge resistence against accidental failures. Even although a simultaneous elimination of almost the 50 percent of all of nodes happened, the network (as a whole) and its function, rarely suffers disruptions
      Perhaps it is neccesary to remark that the notion of robustness in complex systems is a bit different of the popular meaning.
      Altough intuition can tell us that the breakdown of a substantial number of nodes in a network result in an inevitable fragmentation, this is only true for random networks. In scale-free network, we find out a different situation: here, hubs constitute the real network’s scaffolding, so if we randomly eliminate some nodes, it is not going to disrupt the topology significantly, because these networks contain few links compared with the hubs, which connect to nearly everything.

      Best!

      • I understand now, thank you for clarifying. I´m still finding it difficult to cope with all this terminology though…For instance, when referring to ecosystem ascendancy -as in Johnson et al-, would we be talking of some kind of robustness measurement too?

        Thanks again for a great insight in the matter!

  3. Thank you for some useful info on an interesting topic. Just image how these patterns can change with the also changing habits and use of these networks. Keep up tje good work!

    • Thank you!I completely agree, their evolution over time is simply fascinating, especially when relating to an increase in robustness or attack survivability as Manuel was saying. Hope to count on your great reading!

  4. Hey I was just discussing about the 6 degree of separation with my colleague recently and a big surprised to have found this on your article too! It’s great to see how social networking could do a lot of things. Thanks for the very interesting work! though it seems to be a new thing for me!

    • Thank you for the nice words!I´m glad you enjoyed it. This kind of analysis does certainly have a wide range of applications, and I´m hoping to give further reviews on the topic in the near future, so keep an eye on our blog!

  5. Pingback: Measuring Disease Trends through Social Media | FreshBiostats

  6. Pingback: …a scientific crowd | FreshBiostats

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