🎯 https://comdig.unam.mx/2016/12/02/visual-analysis-of-nonlinear-dynamical-systems-chaos-fractals-self-similarity-and-the-limits-of-prediction/

We are looking for the next generation of Network Scientists.

🎯 https://johncarlosbaez.wordpress.com/2016/11/29/compositionality-in-network-theory/

👆 Live streaming video of the whole workshop here:

https://t.co/alDffhTQRW

Right now: Prakash Panagaden on "semantics for physicists".

🎯 Winter school @UniofGreenwich in London 16-20 Jan. Courses on networks and big-data:

https://showtime.gre.ac.uk/index.php/business/CBNAAS17/schedConf/program

🌀 https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-2?utm_medium=email&utm_source=sailthru&utm_campaign=studentnewsletter-20161206&utm_content=course-announcements-mailing-list&utm_term=Newsletter%20Actives

🎯 Stealing Reality: When Criminals Become Data Scientists (or Vice Versa)

http://web.media.mit.edu/~yanival/IEEE_Intelligent_Systems.pdf

🌀 مکانیک کوانتومی وآشوب

http://www.sitpor.org/2015/02/quantum-mechanics-and-chaos/

📄 #Complexity and #Philosophy

Francis Heylighen, Paul Cilliers, Carlos Gershenson
(Submitted on 19 Apr 2006)

https://arxiv.org/pdf/cs/0604072v1

📌 ABSTRACT
The science of complexity is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge. This paper reviews the historical development of this new world view, focusing on its philosophical foundations. Determinism was challenged by quantum mechanics and chaos theory. Systems theory replaced reductionism by a scientifically based holism. Cybernetics and postmodern social science showed that knowledge is intrinsically subjective. These developments are being integrated under the header of "complexity science". Its central paradigm is the multi-agent system. Agents are intrinsically subjective and uncertain about their environment and future, but out of their local interactions, a global organization emerges. Although different philosophers, and in particular the postmodernists, have voiced similar ideas, the paradigm of complexity still needs to be fully assimilated by philosophy. This will throw a new light on old philosophical issues such as relativism, ethics and the role of the subject.

📽 https://youtube.com/watch?index=98&t=139s&v=c867FlzxZ9Y&list=PL2c_ujuXhAvQO18KrtERRMjAkjgr-PQ1k

📄 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)

📽 http://www.namasha.com/v/zxlRvpy9

📽 https://www.youtube.com/shared?ci=-Si29BGKbsA

🎯 https://www.scientificamerican.com/article/big-data-needs-big-theory/?wt.mc=SA_Twitter-Share

📖 http://press.princeton.edu/titles/4308.html

🎯 https://www.santafe.edu/news-center/news/friend-friend-dense-network

🎯 Please write to me or pass this offer on to a possible candidate:

Vacant Ph.D. position at Maastricht University, Netherlands, in Systems Medicine and Network Pharmacology to analyse the common mechanism network based on Albert-Laszlo Barabasi's diseasome for drug target and biomarker discovery. The enthusiastic candidate should have excellent bioinformatics, network analysis and programming skills. Together with outstanding international collaborators our main goals are to re-definde diseases in a mechanism-based manner, develop a drug-based network (drugome) and hypotheses for drug repurposing, which will then be validated within an interdisciplinary team.

Contact: h.schmidt@maastrichtuniversity.nl

📄 Statistical physics of vaccination

Zhen Wang, Chris T. Bauch, Samit Bhattacharyya, Alberto d'Onofrio, Piero Manfredi, Matjaz Perc,Nicola Perra, Marcel Salathé, Dawei Zhao

https://arxiv.org/pdf/1608.09010v3

📌 ABSTRACT
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

Comments:150 pages, 42 figures; published in Physics ReportsSubjects:Physics and #Society (physics.soc-ph); #Statistical_Mechanics (cond-mat.stat-mech); Social and Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE); Applications (stat.AP)

#سلسله_سمینارهای_هفتگی گروه سیستم های پیچیده شهید بهشتی
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl

Boolean network - Wikipedia
https://en.wikipedia.org/wiki/Boolean_network

Boolean Network Models