📽Big Network Visualization and community detection using Gephi
https://www.youtube.com/watch?v=FLiv3xnEepw
https://www.youtube.com/watch?v=FLiv3xnEepw
YouTube
GEPHI - Network visualization tutorial [HD]
Introduction to network analysis and visualization with GEPHI. New video with voiceover available here : https://www.youtube.com/watch?v=GXtbL8avpik !
Datasets and tutorial here: http://www.martingrandjean.ch/gephi-introduction
Papers using Gephi: http:…
Datasets and tutorial here: http://www.martingrandjean.ch/gephi-introduction
Papers using Gephi: http:…
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Is Facebook the New Telescope?
🎯 «فیزیکطوری™ 🔭» یک گروه دانشگاهی با اعضای حرفهای، باحال، با حوصله و کنجکاوه که علم رو به صورت «#حرفهای» دنبال میکنند.
🚩 هدف این گروه خلاقبودن در #تولید محتوای علمی هست، نه #کپی کردن از گروهها یا کانالهای دیگه! هر چیز مرتبط با فیزیک که #منبع موثقی داشته باشه میتونه به این گروه فرستاده بشه، به شرطی که تحت عنوان «#شبهعلم» طبقهبندی نشه!
🔗 لینک گروه:
https://telegram.me/joinchat/BBtzwD0S6ff2F8Rz9eBJ6Q
لطفا این لینک رو برای هر کس که میفرستید، قبلش از شرایط و سیاستهای گروه آگاهش کنید. ما دوستداریم کسایی که حضور و فعالیتشون به گروه کمک میکنه در گروه حضور پیدا کنند!
🚩 هدف این گروه خلاقبودن در #تولید محتوای علمی هست، نه #کپی کردن از گروهها یا کانالهای دیگه! هر چیز مرتبط با فیزیک که #منبع موثقی داشته باشه میتونه به این گروه فرستاده بشه، به شرطی که تحت عنوان «#شبهعلم» طبقهبندی نشه!
🔗 لینک گروه:
https://telegram.me/joinchat/BBtzwD0S6ff2F8Rz9eBJ6Q
لطفا این لینک رو برای هر کس که میفرستید، قبلش از شرایط و سیاستهای گروه آگاهش کنید. ما دوستداریم کسایی که حضور و فعالیتشون به گروه کمک میکنه در گروه حضور پیدا کنند!
🔹 TEXTBOOK
🔶 Critical phenomena in natural sciences: Chaos, fractals, self-organization, and disorder
Description From publisher: Concepts, methods and techniques of statistical physics in the study of correlated, as well as uncorrelated, phenomena are being applied ever increasingly in the natural sciences, biology and economics in an attempt to understand and model the large variability and risks of phenomena. This is the first textbook written by a well-known expert that provides a modern up-to-date introduction for workers outside statistical physics. The emphasis of the book is on a clear understanding of concepts and methods, while it also provides the tools that can be of immediate use in applications. Although this book evolved out of a course for graduate students, it will be of great interest to researchers and engineers, as well as to post-docs in geophysics and meteorology.
http://amzn.to/2l1DQ0h
🔶 Critical phenomena in natural sciences: Chaos, fractals, self-organization, and disorder
Description From publisher: Concepts, methods and techniques of statistical physics in the study of correlated, as well as uncorrelated, phenomena are being applied ever increasingly in the natural sciences, biology and economics in an attempt to understand and model the large variability and risks of phenomena. This is the first textbook written by a well-known expert that provides a modern up-to-date introduction for workers outside statistical physics. The emphasis of the book is on a clear understanding of concepts and methods, while it also provides the tools that can be of immediate use in applications. Although this book evolved out of a course for graduate students, it will be of great interest to researchers and engineers, as well as to post-docs in geophysics and meteorology.
http://amzn.to/2l1DQ0h
Listen to 050 - Complexity Science is Everyone’s Science by HumanCurrent #np on #SoundCloud
https://soundcloud.com/humancurrent/050-complexity-science-is
https://soundcloud.com/humancurrent/050-complexity-science-is
SoundCloud
Complexity Science is Everyone’s Science
In this episode, Haley interviews professor, complexity scientist, and founding president of the New England Complex Systems Institute (NECSI), Yaneer Bar-Yam. Yaneer talks about how we can understand
یک کورس مقدماتی برای هر علاقمند به سیستمهای پیچیده از
Complexity Explorer
موسسه سانتافه.
این کورس هیچ پیشنیاز خاص ریاضی یا فیزیک نداره و تنها چیزی که نیازه داشتن علاقه و انگیزه برای آشنایی با حوزه پیچیدگی هست:
https://www.complexityexplorer.org/courses/74-introduction-to-complexity-spring-2017
Complexity Explorer
موسسه سانتافه.
این کورس هیچ پیشنیاز خاص ریاضی یا فیزیک نداره و تنها چیزی که نیازه داشتن علاقه و انگیزه برای آشنایی با حوزه پیچیدگی هست:
https://www.complexityexplorer.org/courses/74-introduction-to-complexity-spring-2017
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IntroSpring2017PromoVideo
🔹 Do we need to #age? NECSI challenges the mathematical assumptions of traditional evolutionary theory and shows #aging is programmed, and not an inherent biological breakdown.
http://www.necsi.edu/research/overview/evolutionoflifespans.html
http://www.necsi.edu/research/overview/evolutionoflifespans.html
⭕️ Entropy, an idea often equated with disorder, can actually organize things:
https://www.quantamagazine.org/20170308-digital-alchemist-sharon-glotzer-interview-emergence/
https://www.quantamagazine.org/20170308-digital-alchemist-sharon-glotzer-interview-emergence/
Quanta Magazine
Digital Alchemist’ Sharon Glotzer Seeks Rules of Emergence | Quanta Magazine
Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.
🗞 Topological Data Analysis of Financial Time Series: Landscapes of Crashes
Marian Gidea, Yuri Katz
🔗 https://arxiv.org/pdf/1703.04385
📌 ABSTRACT
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which goes beyond the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.
Marian Gidea, Yuri Katz
🔗 https://arxiv.org/pdf/1703.04385
📌 ABSTRACT
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which goes beyond the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.
🗞 Maximum entropy sampling in complex networks
Filippo Radicchi, Claudio Castellano
🔗 https://arxiv.org/pdf/1703.03858
📌 ABSTRACT
Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. Here, we introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. We show that the method is effective for various processes on different substrates. We further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.
Filippo Radicchi, Claudio Castellano
🔗 https://arxiv.org/pdf/1703.03858
📌 ABSTRACT
Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. Here, we introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. We show that the method is effective for various processes on different substrates. We further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.
🔹Beyond Big Data: Identifying Important Information for Real World Challenges
http://necsi.edu/projects/yaneer/information/?platform=hootsuite
http://necsi.edu/projects/yaneer/information/?platform=hootsuite