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

Diamond vs Graphite (Donald Sadoway, MIT)

"Network Wanderings in Paris" A summary of some of the great talks at #NetSci2018: https://t.co/UCNQsxncoD.

🌀 https://softbites.org/2018/06/22/a-storm-of-oscillators/amp/?__twitter_impression=true

#اطلاع_رسانی_سمینارهای_دانشکده
#سمينار_فيزیک_آماری #سمینار_ماده_چگال_نرم
امروز آقای متئو مارسیلی از عبدالسلام سخنرانی‌ با موضوع زیر ارائه می‌دهند.(یکشنبه سوم تیر ساعت ۱۵:۰۰ اتاق ۴۱۲ تالار پرتوی)

How simple are simple spin models?

The talk is about spin Hamiltonians with interactions of arbitrary order. An information
theoretic classification of these models into complexity classes will be discussed. The
results show that the information theoretic notion of a ``simple model’’ conforms to the
one assumed in physics. Applications to high dimensional inference will also be
discussed.

@anjoman_elmi_phys_sut

♦️ آزمایشگاه ملی نقشه برداری مغز برگزار میکند:

🔹دومین کارگاه مبانی پردازش سیگنالهای حیاتی با نرم افزار متلب با محوریت علوم اعصاب محاسباتی

🔹 اطلاعات بیشتر و ثبت نام در:
https://goo.gl/Xi13Tk

Claudio Castellano' slides on #spreading_processes on complex networks.
24th Annual #IASBS Meeting on Condensed Matter Physics & School on Complex Systems.

👌 Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

https://graph-tool.skewed.de/

🌀 "A data scientist is a statistician who lives in San Francisco"

What’s the Difference Between Data Science and Statistics?

https://t.co/7zHlPKNf5u