🗞 Network susceptibilities: Theory and applications
Debsankha Manik, Martin Rohden, Henrik Ronellenfitsch, Xiaozhu Zhang, Sarah Hallerberg, Dirk Witthaut, and Marc Timme (2017)
Phys. Rev. E 95(1):012319.
Projects involved: Dynamics of Modern Power Grids
🔗 https://www.researchgate.net/publication/308107316_Network_susceptibilities_Theory_and_applications
📌 ABSTRACT
We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.
Debsankha Manik, Martin Rohden, Henrik Ronellenfitsch, Xiaozhu Zhang, Sarah Hallerberg, Dirk Witthaut, and Marc Timme (2017)
Phys. Rev. E 95(1):012319.
Projects involved: Dynamics of Modern Power Grids
🔗 https://www.researchgate.net/publication/308107316_Network_susceptibilities_Theory_and_applications
📌 ABSTRACT
We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.
ResearchGate
(PDF) Network susceptibilities: Theory and applications
PDF | We introduce the concept of network susceptibilities quantifying the response of the collective dy- namics of a network to small parameter... | Find, read and cite all the research you need on ResearchGate
🌀 Multilayer Networks Library for Python (Pymnet)¶
http://www.mkivela.com/pymnet/
The libarary is based on the general definition of multilayer networks presented in a review article. Multilayer networks can be used to represent various types network generalizations found in the literature. For example, multiplex networks, temporal networks, networks of networks, and interdependent networks are all types of multilayer networks. The library supports even more general types of networks with multiple aspects such that the networks can for example have both temporal and multiplex aspect at the same time.
The visualization on the left is produced with the library. See the Visualizing networks tutorial (http://www.mkivela.com/pymnet/visualizing.html#visualization-tutorial) for instructions how to your own network data with the library!
http://www.mkivela.com/pymnet/
The libarary is based on the general definition of multilayer networks presented in a review article. Multilayer networks can be used to represent various types network generalizations found in the literature. For example, multiplex networks, temporal networks, networks of networks, and interdependent networks are all types of multilayer networks. The library supports even more general types of networks with multiple aspects such that the networks can for example have both temporal and multiplex aspect at the same time.
The visualization on the left is produced with the library. See the Visualizing networks tutorial (http://www.mkivela.com/pymnet/visualizing.html#visualization-tutorial) for instructions how to your own network data with the library!
🎞 https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
This course is taught by Nando de Freitas. (Slides + Video)
This course is taught by Nando de Freitas. (Slides + Video)
www.cs.ox.ac.uk
Machine Learning
⚡️ Introduction to Renormalization
Lead instructor: Simon DeDeo
https://www.complexityexplorer.org/tutorials/67-introduction-to-renormalization
🌀 About the Tutorial:
What does a JPEG have to do with economics and quantum gravity? All of them are about when happens when you simplify world-descriptions. A JPEG compresses an image by throwing out fine structure in ways a casual glance won't detect. Economists produce theories of human behavior that gloss over the details of individual psychology. Meanwhile, even our most sophisticated physics experiments can't show us the most fundamental building-blocks of matter, and so our theories have to make do with descriptions that blur out the smallest scales. The study of how theories change as we move to more or less detailed descriptions is known as renormalization.
This tutorial provides a modern introduction to renormalization from a complex systems point of view. Simon DeDeo will take students from basic concepts in information theory and image processing to some of the most important concepts in complexity, including emergence, coarse-graining, and effective theories. Only basic comfort with the use of probabilities is required for the majority of the material; some more advanced modules rely on more sophisticated algebra and basic calculus, but can be skipped.
We'll introduce, in an elementary fashion, explicit examples of model-building including Markov Chains and Cellular Automata. We'll cover some new ideas for the description of complex systems including the Krohn-Rhodes theorem and State-Space Compression. And we'll show the connections between classic problems in physics, including the Ising model and plasma physics, and cutting-edge questions in machine learning and artificial intelligence.
https://www.complexityexplorer.org/tutorials/67-introduction-to-renormalization
Lead instructor: Simon DeDeo
https://www.complexityexplorer.org/tutorials/67-introduction-to-renormalization
🌀 About the Tutorial:
What does a JPEG have to do with economics and quantum gravity? All of them are about when happens when you simplify world-descriptions. A JPEG compresses an image by throwing out fine structure in ways a casual glance won't detect. Economists produce theories of human behavior that gloss over the details of individual psychology. Meanwhile, even our most sophisticated physics experiments can't show us the most fundamental building-blocks of matter, and so our theories have to make do with descriptions that blur out the smallest scales. The study of how theories change as we move to more or less detailed descriptions is known as renormalization.
This tutorial provides a modern introduction to renormalization from a complex systems point of view. Simon DeDeo will take students from basic concepts in information theory and image processing to some of the most important concepts in complexity, including emergence, coarse-graining, and effective theories. Only basic comfort with the use of probabilities is required for the majority of the material; some more advanced modules rely on more sophisticated algebra and basic calculus, but can be skipped.
We'll introduce, in an elementary fashion, explicit examples of model-building including Markov Chains and Cellular Automata. We'll cover some new ideas for the description of complex systems including the Krohn-Rhodes theorem and State-Space Compression. And we'll show the connections between classic problems in physics, including the Ising model and plasma physics, and cutting-edge questions in machine learning and artificial intelligence.
https://www.complexityexplorer.org/tutorials/67-introduction-to-renormalization
#سمینارهای_هفتگی گروه سیستمهای پیچیده و علم شبکه دانشگاه شهید بهشتی
🔹دوشنبه، ۲۸ فروردینماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
🔹دوشنبه، ۲۸ فروردینماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
🌀 Inferential Statistics
https://www.coursera.org/learn/inferential-statistics-intro
📌 About this course:
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
https://www.coursera.org/learn/inferential-statistics-intro
📌 About this course:
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
🌀 Applied Social Network Analysis in Python
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
💡A brief history of deep learning, including the rise, fall, and renaissance of neural networks:
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Clear, informative article on #neural_networks and #deep_learning, with lots of good historical background and insights.
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Clear, informative article on #neural_networks and #deep_learning, with lots of good historical background and insights.
MIT News
Explained: Neural networks
“Deep learning,” the machine-learning technique behind the best-performing artificial-intelligence systems of the past decade, is really a revival of the 70-year-old concept of neural networks.
🗞 Modeling interactions between political parties and electors
F. Bagarello, F. Gargano
🔗 https://arxiv.org/abs/1704.05453
📌 ABSTRACT
In this paper we extend some recent results on an operatorial approach to the description of alliances between political parties interacting among themselves and with a basin of electors. In particular, we propose and compare three different models, deducing the dynamics of their related {\em decision functions}, i.e. the attitude of each party to form or not an alliance. In the first model the interactions between each party and their electors are considered. We show that these interactions drive the decision functions towards certain asymptotic values depending on the electors only: this is the {\em perfect party}, which behaves following the electors' suggestions. The second model is an extension of the first one in which we include a rule which modifies the status of the electors, and of the decision functions as a consequence, at some specific time step. In the third model we neglect the interactions with the electors while we consider cubic and quartic interactions between the parties and we show that we get (slightly oscillating) asymptotic values for the decision functions, close to their initial values. This is the {\em real party}, which does not listen to the electors. Several explicit situations are considered in details and numerical results are also shown.
F. Bagarello, F. Gargano
🔗 https://arxiv.org/abs/1704.05453
📌 ABSTRACT
In this paper we extend some recent results on an operatorial approach to the description of alliances between political parties interacting among themselves and with a basin of electors. In particular, we propose and compare three different models, deducing the dynamics of their related {\em decision functions}, i.e. the attitude of each party to form or not an alliance. In the first model the interactions between each party and their electors are considered. We show that these interactions drive the decision functions towards certain asymptotic values depending on the electors only: this is the {\em perfect party}, which behaves following the electors' suggestions. The second model is an extension of the first one in which we include a rule which modifies the status of the electors, and of the decision functions as a consequence, at some specific time step. In the third model we neglect the interactions with the electors while we consider cubic and quartic interactions between the parties and we show that we get (slightly oscillating) asymptotic values for the decision functions, close to their initial values. This is the {\em real party}, which does not listen to the electors. Several explicit situations are considered in details and numerical results are also shown.
Evolutionary dynamics on any population structure
http://www.nature.com/nature/journal/v544/n7649/full/nature21723.html
http://www.nature.com/nature/journal/v544/n7649/full/nature21723.html