Publication by Dr. Fragkiskos Papadopoulos, Assistant Professor in Computer Engineering and Informatics, in Nature Physics!


Dr. Fragkiskos Papadopoulos and colleagues from the University of Barcelona have published their study titled "Hidden geometric correlations in real multiplex networks" in Nature Physics.

http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3812.html

Nature Physics publishes landmark papers of the highest quality and significance in all areas of physics, pure and applied. The journal reflects core physics disciplines, but is also open to a broad range of topics whose central theme falls within the bounds of physics. Its impact factor is 18.791.
The work shows that networked systems from drastically different domains, ranging from brain networks, to social networks and the Internet, are multilayer systems whose layers are coupled via hidden geometric correlations. As shown, these correlations yield a very powerful and general framework for understanding and analyzing real multilayer systems, and facilitating important applications. These applications range from detecting sets of network nodes that are simultaneously similar in multiple layers, to predicting connections in one layer by observing the hidden geometric space of another layer, to enabling efficient targeted navigation in a multilayer system using only local topology knowledge.

 

Publication by Dr. Fragkiskos Papadopoulos, Assistant Professor in Computer Engineering and Informatics, in Nature Physics!

Dr. Fragkiskos Papadopoulos and colleagues from the University of Barcelona have published their study titled "Hidden geometric correlations in real multiplex networks" in Nature Physics.

http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3812.html

Nature Physics publishes landmark papers of the highest quality and significance in all areas of physics, pure and applied. The journal reflects core physics disciplines, but is also open to a broad range of topics whose central theme falls within the bounds of physics. Its impact factor is 18.791.
The work shows that networked systems from drastically different domains, ranging from brain networks, to social networks and the Internet, are multilayer systems whose layers are coupled via hidden geometric correlations. As shown, these correlations yield a very powerful and general framework for understanding and analyzing real multilayer systems, and facilitating important applications. These applications range from detecting sets of network nodes that are simultaneously similar in multiple layers, to predicting connections in one layer by observing the hidden geometric space of another layer, to enabling efficient targeted navigation in a multilayer system using only local topology knowledge.