🌀 https://medium.com/complex-systems-channel/1-4-universality-1e51ec144390#.qhgfoy49g

🔗 http://www.necsi.edu/research/multiscale/universalitytext.pdf

🗞 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.012307

🎞 http://www.aparat.com/v/H2azX

🎞 http://www.aparat.com/v/yT7X4

#مدرسه سه روزه #علم_داده (Data science)

🗓 مرداد و شهريور 96
📍دانشگاه شهید بهشتی
درصورت نیاز،خوابگاه به شرکت کنندگان تعلق میگیرد!

منتظر اطلاعیه های بعدی باشید...
انجمن علمی فیزیک بهشتی
@sbu_physics

🗞 Community Discovery in Dynamic Networks: a Survey

Giulio Rossetti, Rémy Cazabet

🔗 https://arxiv.org/pdf/1707.03186

📌 ABSTRACT
Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented

🔸 https://www.nature.com/news/ant-colonies-flow-like-fluid-to-build-tall-towers-1.22290
🎞👇

Ant architecture: The simple rules of ant construction

🔸 http://news.harvard.edu/gazette/story/2013/10/as-complex-as-a-toy/

Summer School on Complex Socio-Technical Systems, 4-8/09
#IFISC10years #SocioComplex17

https://sociocomplex2017.ifisc.uib-csic.es/

🎞 http://www.aparat.com/v/LphAK

Dr. Paul Hooper challenges Dr. Eleanor Power to discuss how religion and suffering bring people together while eating mouth scalding New Mexico green chile!

🔅 http://www.cidid.org/publications-1/2017/7/14/monte-carlo-profile-confidence-intervals-for-dynamic-systems

🔗 https://static1.squarespace.com/static/53d8f599e4b0ba978ec978d1/t/5969291bc534a5cc075a1e3f/1500064029155/20170126.full.pdf

Thomas House explains the maths behind memes

Whoosh! Whoosh!
#CollectiveBehavior

🗞 Structure-based control of complex networks with nonlinear dynamics

Jorge Gomez Tejeda Zañudo, Gang Yang, Réka Albert

🔗 http://pnas.org/content/114/28/7234.abstract.html?etoc

🚩 Significance
Many biological, technological, and social systems can be encoded as networks over which nonlinear dynamical processes such as cell signaling, information transmission, or opinion spreading take place. Despite many advances in network science, we do not know to what extent the network architecture shapes our ability to control these nonlinear systems. Here we extend a recently developed control framework that addresses this question and apply it to real networks of diverse types. Our results highlight the crucial role of a network’s feedback structure in determining robust control strategies, provide a dynamic-detail-independent benchmark for other control methods, and open up a promising research direction in the control of complex networks with nonlinear dynamics.

📌 ABSTRACT
What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

🎞 http://www.aparat.com/v/tJos2

(slides: 👇)