🔗 https://arxiv.org/pdf/1806.05977

🎞 https://youtu.be/RZGdnAM9Kis

🌀 https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7

⭕ https://news.northeastern.edu/2018/06/18/tired-of-being-manipulated-by-fake-news-this-northeastern-researcher-is-creating-the-tools-we-need-to-fight-against-it/

🧗‍♂ https://www.nature.com/articles/d41586-018-05444-y

Network visualization with R: updated tutorial from #polnet2018. Includes visualization basics, interactive and animated networks, temporal graphs and networks on geographic maps: https://t.co/Ro39rk0

24th Annual #IASBS Meeting on Condensed Matter Physics & School on Complex Systems. https://t.co/c23KC2IKx8

🔖 Thermodynamics of the Minimum Denoscription Length on Community Detection

Juan Ignacio Perotti, Claudio Juan Tessone, Aaron Clauset, Guido Caldarelli

🔗 https://arxiv.org/pdf/1806.07005.pdf

📌 ABSTRACT
Modern statistical modeling is an important complement to the more traditional approach of physics where Complex Systems are studied by means of extremely simple idealized models. The Minimum Denoscription Length (MDL) is a principled approach to statistical modeling combining Occam's razor with Information Theory for the selection of models providing the most concise denoscriptions. In this work, we introduce the Boltzmannian MDL (BMDL), a formalization of the principle of MDL with a parametric complexity conveniently formulated as the free-energy of an artificial thermodynamic system. In this way, we leverage on the rich theoretical and technical background of statistical mechanics, to show the crucial importance that phase transitions and other thermodynamic concepts have on the problem of statistical modeling from an information theoretic point of view. For example, we provide information theoretic justifications of why a high-temperature series expansion can be used to compute systematic approximations of the BMDL when the formalism is used to model data, and why statistically significant model selections can be identified with ordered phases when the BMDL is used to model models. To test the introduced formalism, we compute approximations of BMDL for the problem of community detection in complex networks, where we obtain a principled MDL derivation of the Girvan-Newman (GN) modularity and the Zhang-Moore (ZM) community detection method. Here, by means of analytical estimations and numerical experiments on synthetic and empirical networks, we find that BMDL-based correction terms of the GN modularity improve the quality of the detected communities and we also find an information theoretic justification of why the ZM criterion for estimation of the number of network communities is better than alternative approaches such as the bare minimization of a free energy.

💰 Master in Physics of Complex Systems: #IFISC offers a limited number of mobility fellowships. Each one amounts to 6000 euros plus tuition fees. Conditions for these grants can be found in the following link: https://t.co/d629pY2JNs

🔖 How to Maximize the Spread of Social Influence: A Survey

Giuseppe De Nittis, Nicola Gatti

🔗 https://arxiv.org/pdf/1806.07757.pdf

📌 ABSTRACT
This survey presents the main results achieved for the influence maximization problem in social networks. This problem is well studied in the literature and, thanks to its recent applications, some of which currently deployed on the field, it is receiving more and more attention in the scientific community. The problem can be formulated as follows: given a graph, with each node having a certain probability of influencing its neighbors, select a subset of vertices so that the number of nodes in the network that are influenced is maximized. Starting from this model, we introduce the main theoretical developments and computational results that have been achieved, taking into account different diffusion models describing how the information spreads throughout the network, various ways in which the sources of information could be placed, and how to tackle the problem in the presence of uncertainties affecting the network. Finally, we present one of the main application that has been developed and deployed exploiting tools and techniques previously discussed.

✔ https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.062136

https://www.crcpress.com/Nonlinear-Dynamics-and-Chaos-With-Applications-to-Physics-Biology-Chemistry/Strogatz/p/book/9780813349107?utm_source=twitter&utm_medium=post&utm_campaign=180616477https://www.

🎞 Topology matters:
https://m.youtube.com/watch?v=X6Ilrw32EKI