📝 Community detection in networks: A user guide
Santo FortunatoDarko
http://www.sciencedirect.com/science/article/pii/S0370157316302964
📌 Abstract
Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.
Keywords
#Networks #Communities #Clustering
Santo FortunatoDarko
http://www.sciencedirect.com/science/article/pii/S0370157316302964
📌 Abstract
Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.
Keywords
#Networks #Communities #Clustering
📝 Cellular Automata and Complexity: Collected Papers
https://www.complexityexplorer.org/explore/resources/461-cellular-automata-and-complexity-collected-papers
📌 Book Description:
"Are mathematical equations the best way to model nature? For many years it had been assumed that they were. But in the early 1980s, Stephen Wolfram made the radical proposal that one should instead build models that are based directly on simple computer programs. Wolfram made a detailed study of a class of such models known as cellular automata, and discovered a remarkable fact: that even when the underlying rules are very simple, the behavior they produce can be highly complex, and can mimic many features of what we see in nature. And based on this result, Wolfram began a program to develop what has become A New Kind of Science." "The results of Wolfram's work found many applications, from the so-called Wolfram Classification central to fields such as artificial life, to new ideas about cryptography and fluid dynamics. This book is a collection of Wolfram's original papers on cellular automata and complexity. Some of these papers are widely known in the scientific community; others have never been published before. Together, the papers provide a highly readable account of what has become a major new field of science, with important implications for physics, biology, economics, computer science and many other areas."
https://www.complexityexplorer.org/explore/resources/461-cellular-automata-and-complexity-collected-papers
📌 Book Description:
"Are mathematical equations the best way to model nature? For many years it had been assumed that they were. But in the early 1980s, Stephen Wolfram made the radical proposal that one should instead build models that are based directly on simple computer programs. Wolfram made a detailed study of a class of such models known as cellular automata, and discovered a remarkable fact: that even when the underlying rules are very simple, the behavior they produce can be highly complex, and can mimic many features of what we see in nature. And based on this result, Wolfram began a program to develop what has become A New Kind of Science." "The results of Wolfram's work found many applications, from the so-called Wolfram Classification central to fields such as artificial life, to new ideas about cryptography and fluid dynamics. This book is a collection of Wolfram's original papers on cellular automata and complexity. Some of these papers are widely known in the scientific community; others have never been published before. Together, the papers provide a highly readable account of what has become a major new field of science, with important implications for physics, biology, economics, computer science and many other areas."
🔵 From Navigation on the High Seas to Magnets at High Temperatures
How Conformal Maps Enter Our Lives
https://sinews.siam.org/DetailsPage/TabId/900/ArtMID/2243/ArticleID/1738/From-Navigation-on-the-High-Seas-to-Magnets-at-High-Temperatures.aspx
#CFT
How Conformal Maps Enter Our Lives
https://sinews.siam.org/DetailsPage/TabId/900/ArtMID/2243/ArticleID/1738/From-Navigation-on-the-High-Seas-to-Magnets-at-High-Temperatures.aspx
#CFT
SIAM News
From Navigation on the High Seas to Magnets at High Temperatures
Just imagine that you have attended the SIAM Conference on Industrial and Applied Geometry (GD29) in San Francisco and you are aboard a plane that is taking you back home to your research institute in Berlin. You see on the monitor in front of you that the…
📝 The physics of multilayer networks
Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas
https://arxiv.org/pdf/1604.02021v1
📌 ABSTRACT
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.
Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas
https://arxiv.org/pdf/1604.02021v1
📌 ABSTRACT
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.
🔴 The Paper Shredder
Great, curated collection of recent papers about complex systems and networks
http://www.uvm.edu/~cmplxsys/teaching-learning/papershredder/
Great, curated collection of recent papers about complex systems and networks
http://www.uvm.edu/~cmplxsys/teaching-learning/papershredder/
Vermont Complex Systems Center
The Paper Shredder
Every two weeks, and in the grand tradition of courageous academics everywhere, we review the painstaking, meticulous work of others not present to defend themselves. Pure of motivation, we endeavo…
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A New Spin on the Quantum Brain
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
Complex Systems Studies
A New Spin on the Quantum Brain A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
🎯 https://www.quantamagazine.org/20161102-quantum-neuroscience/
#Neuroscience #Quantum_Mechanics #Biology #Chemistry #Nuclear_Physics
#Neuroscience #Quantum_Mechanics #Biology #Chemistry #Nuclear_Physics
Quanta Magazine
A New Spin on the Quantum Brain
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
#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
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
اعلام برنامه های سلسله سمینارهای بیوفیزیک پژوهشکده IBB دانشگاه تهران:
🔵 A great review of #networkscience and neurology by @AmarDhand
http://www.nature.com/nrneurol/journal/v12/n10/full/nrneurol.2016.119.html
http://www.nature.com/nrneurol/journal/v12/n10/full/nrneurol.2016.119.html
🔵 🎓 Data Science MOOCs starting this week!
http://us8.campaign-archive1.com/?u=c4e45e47176fffe244e832ba8&id=14066ba5e9
http://us8.campaign-archive1.com/?u=c4e45e47176fffe244e832ba8&id=14066ba5e9
📝 Sensitive dependence of network dynamics on network structure
Takashi Nishikawa, Jie Sun, Adilson E. Motter
https://arxiv.org/pdf/1611.01164v1
📌 ABSTRACT
The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
#Adaptation and #Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph)
Takashi Nishikawa, Jie Sun, Adilson E. Motter
https://arxiv.org/pdf/1611.01164v1
📌 ABSTRACT
The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
#Adaptation and #Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph)