Index of Complex Networks – The “Google for Networks” by University of Colorado Boulder

This week at the Conference on Network Science (NetSci 2016), Aaron Clauset from the University of Colorado Boulder unveiled the Index of Complex Networks, assembling information about thousands of network datasets available online. They have about 3500 entries at the moment, with many more to come in the near future.

This news is a big deal in the network science community. Researchers in the field of Network Science are (quite obviously) using network datasets on a daily basis, and anything that helps them get their hands on more of them is good news. Until now, there have been several sites collecting network datasets, such as SNAP from Jure Leskovec’s group at Stanford and KONECT written by me in at the University in Koblenz.

The new ICON website takes the community one step further in making available not the datasets themselves, but an index of them. I.e., a listing of datasets available on the web, whose largest share comes from KONECT, but also from SNAP and countless other network dataset-publishing sources.

What does this mean for the field of Network Science? It means we will be able to verify our claims not on individual datasets, or on a few dozens ones, but on thousands of them. This is worth spelling out: With thousands of datasets, we can finally investigate the statistical significance of claims. We may finally find out whether social networks are really scale-free. We may finally find out, whether social networks really have so many more triangles than other networks, as claimed traditionally in social network studies. In fact, we already have first results based on the ICON data: Networks as a whole are not scale-free. In other words, degree distributions are not power laws in a statistical sense. This contradicts many claims made in the field.

I expect this data to lead to many new insights. In particular, many “well-known” facts about networks will have to be revised. I have to say, I’m very much looking forward to start this new chapter of Network Science.

The Index of Complex Networks (ICON) is here: https://icon.colorado.edu/

KONECT (The Koblenz Network Collection) is here: http://konect.uni-koblenz.de/

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