📄 Hidden geometric correlations in real multiplex networks

Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos

http://www.nature.com/nphys/journal/v12/n11/full/nphys3812.html

📌 ABSTRACT:
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.

Subject terms:

#Applied_physics
#Complex_networks
#Statistics

📄 Big data need physical ideas and methods

https://arxiv.org/pdf/1412.6848v1

📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.

#Data_Analysis , #Statistics and #Probability (physics.data-an)

📝 The many facets of community detection in complex networks

Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte

https://arxiv.org/pdf/1611.07769v1

📌 ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.

#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph

⚙ This collection of over 500 MATLAB examples can help you with #machinelearning, #statistics, and #math problems
https://www.mathworks.com/examples/product-group/matlab-math-statistics-and-optimization?s_eid=PSM_da&hootPostID=70cb4d7118cfa4662cc041050f5e8ff1

🎞 Streaming now from SFI: External Professor Amos Golan of Americanunivers on "#Information and Model Misspecification: Classical #Statistics vs. Info-Metrics"

https://t.co/XL340hSKKa

#entropy #likelihood #infometrics #inference #powerlaw

Interested in #InfectiousDiseases #statistics #modeling?

Check out #SISMID Summer Institute in Statistics and Modeling in Infectious Diseases @UWBiostat - offered online this year!

https://t.co/dz9EGnfJeF