THE #PHYSICS OF #INFORMATION
F. ALEXANDER BAIS AND J. DOYNE FARMER

http://arxiv.org/pdf/0708.2837v2.pdf

📝 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

📖 Phenomenological theory of collective decision-making

Anna Zafeiris, Zsombor Koman, Enys Mones, Tamás Vicsek

https://arxiv.org/pdf/1612.00071v1

🔗 ABSTRACT
An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.

Subjects: #Physics and #Society (physics.soc-ph); Social and #Information #Networks

🎯 Samuel Arbesman on #Complex_Adaptive_Systems and the Difference between #Biological and #Physics Based Thinking


https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052

📄 Universality of the SIS prevalence in networks

Piet Van Mieghem


https://arxiv.org/pdf/1612.01386v1

📌 ABSTRACT
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.

Subjects: #Physics and #Society (physics.soc-ph); #Social and #Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE)

🗞 #Physics, #Emergence, and the #Connectome

Robert B. Laugh
Department of Physics, Stanford University, Stanford, CA

🔗 http://www.cell.com/neuron/pdf/S0896-6273(14)00681-3.pdf

📌 SUMMARY:
Experience with complex systems more primitive than the brain teaches important lessons about big data in biology. Chief among them is that physical laws, relationships among measured things that are always true, emerge out of chaos, not the other way around. Correct prediction (as opposed to incorrect prediction) from large data sets requires understanding of these laws. The reason is that the same processes that make them also make the system wildly error-intolerant if the errors are too large. This instability routinely causes computer simulations of even primitive systems to fail by enabling mistakes to cascade into ever worsening falsehoods. The more complex and sophisticated the system is, the more intolerant to errors it becomes.

🎞 https://www.instagram.com/p/BZwC8Y1lnlQ/

🎞 http://www.cornell.edu/video/ben-widom-potential-distribution-theory-statistical-mechanics

Ben Widom, Cornell's Goldwin Smith Professor of Chemistry, discusses potential-distribution theory in statistical mechanics, Feb. 20, 2017.

#physics #chemistry

🔻 Take a look at this #PhD #Fellowship in #LivingMatter #Physics offered at MPI for Dynamics and Self-Organization

https://t.co/QjwgRmYKcY #PhDGermany #TheoreticalPhysics #AppliedMathematics

〽️The statistical mechanics of Twitter

🌐Paper : https://arxiv.org/abs/1812.07029
🎲 @ComplexSys
#Physics #Society #StatisticPhysics