Forwarded from Deleted Account [SCAM]
Visualizing Frustration: Through the Spinning Glass.webm
41.3 MB
Visualizing Frustration: Through the Spinning Glass
Deleted Account
Visualizing Frustration: Through the Spinning Glass.webm
🔹 Visualizing Frustration:
Through the Spinning-Glass
Randy Andrews Mentor: Ruben Andrist
August 15, 2014
📄 http://samoa.santafe.edu/media/cms_page_media/583/randypaper.pdf
Through the Spinning-Glass
Randy Andrews Mentor: Ruben Andrist
August 15, 2014
📄 http://samoa.santafe.edu/media/cms_page_media/583/randypaper.pdf
🔖 An exact method for computing the frustration index in signed networks using binary programming
Samin Aref, Andrew J. Mason, Mark C. Wilson
🔗 https://arxiv.org/pdf/1611.09030
📌 ABSTRACT
Computing the frustration index of a signed graph is a key to solving problems in different fields of research including social networks, physics, material science, and biology. In social networks the frustration index determines network distance from a state of structural balance. Although the definition of frustration index goes back to 1960, an exact algorithmic computation method has not yet been proposed. The main reason seems to be the complexity of computing the frustration index which is closely related to well-known NP-hard problems such as MAXCUT.
New quadratic and linear binary programming models are developed to compute the frustration index exactly. We introduce several speed-up techniques involving prioritised branching, local search heuristics, and valid inequalities inferred from graph structural properties. The computational improvements achieved by implementing the speed-up techniques allow us to calculate the exact values of the frustration index by running the optimisation models in Gurobi solver.
The speed-up techniques make our models capable of processing graphs with thousands of nodes and edges in seconds on inexpensive hardware. The solve time and solution quality comparison against the literature shows the superiority of our models in both random and real signed networks.
Samin Aref, Andrew J. Mason, Mark C. Wilson
🔗 https://arxiv.org/pdf/1611.09030
📌 ABSTRACT
Computing the frustration index of a signed graph is a key to solving problems in different fields of research including social networks, physics, material science, and biology. In social networks the frustration index determines network distance from a state of structural balance. Although the definition of frustration index goes back to 1960, an exact algorithmic computation method has not yet been proposed. The main reason seems to be the complexity of computing the frustration index which is closely related to well-known NP-hard problems such as MAXCUT.
New quadratic and linear binary programming models are developed to compute the frustration index exactly. We introduce several speed-up techniques involving prioritised branching, local search heuristics, and valid inequalities inferred from graph structural properties. The computational improvements achieved by implementing the speed-up techniques allow us to calculate the exact values of the frustration index by running the optimisation models in Gurobi solver.
The speed-up techniques make our models capable of processing graphs with thousands of nodes and edges in seconds on inexpensive hardware. The solve time and solution quality comparison against the literature shows the superiority of our models in both random and real signed networks.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful denoscription of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful denoscription of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
Dynamical Systems and Chaos AND Nonlinear Dynamics MOOCs are open for enrollment! Check them out at:
https://www.complexityexplorer.org/courses
https://www.complexityexplorer.org/courses
🔖 Statistical physics of balance theory
Andres M. Belaza, Kevin Hoefman, Jan Ryckebusch,Aaron Bramson, Milan van den Heuvel, Koen Schoors
🔗 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183696
📌 ABSTRACT
Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads’ incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the “agents” involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data.
Andres M. Belaza, Kevin Hoefman, Jan Ryckebusch,Aaron Bramson, Milan van den Heuvel, Koen Schoors
🔗 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183696
📌 ABSTRACT
Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads’ incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the “agents” involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data.
journals.plos.org
Statistical physics of balance theory
Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively…
🔖 General Framework of Studying Eigenvector Multicentrality in Multilayer Networks
Mincheng Wu, Yongtao Zhang, Shibo He, Jiming Chen, Youxian Sun, Yang-Yu Liu
🔗
https://arxiv.org/pdf/1708.07763
📌 ABSTRACT
Multilayer networks have drawn much attention in the community of network science recently. Tremendous effort has been invested to understand their structure and functions, among which centrality is one of the most effective approaches. While various metrics of centrality have been proposed for single-layer networks, a general framework of studying centrality in multiplayer networks is lacking. Here we introduce a mathematical framework to study eigenvector multicentrality, which enables us to quantify the relationship between interlayer influences and eigenvector multicentrality, providing an analytical tool to describe how eigenvector multicentralities of nodes propagate among different layers. Further, the framework is flexible for integrating prior knowledge of the interplay among layers so as to attain a tailored eigenvector multicentrality for varying scenarios. We show how to model the multilayer influences by choosing appropriate influence weight functions and design algorithms to calculate eigenvector multicentrality in various typical scenarios. We apply this framework to analyze several empirical multilayer networks, finding that it can quantify the influences among layers and describe the structure-function relationship of multilayer networks very well.
Mincheng Wu, Yongtao Zhang, Shibo He, Jiming Chen, Youxian Sun, Yang-Yu Liu
🔗
https://arxiv.org/pdf/1708.07763
📌 ABSTRACT
Multilayer networks have drawn much attention in the community of network science recently. Tremendous effort has been invested to understand their structure and functions, among which centrality is one of the most effective approaches. While various metrics of centrality have been proposed for single-layer networks, a general framework of studying centrality in multiplayer networks is lacking. Here we introduce a mathematical framework to study eigenvector multicentrality, which enables us to quantify the relationship between interlayer influences and eigenvector multicentrality, providing an analytical tool to describe how eigenvector multicentralities of nodes propagate among different layers. Further, the framework is flexible for integrating prior knowledge of the interplay among layers so as to attain a tailored eigenvector multicentrality for varying scenarios. We show how to model the multilayer influences by choosing appropriate influence weight functions and design algorithms to calculate eigenvector multicentrality in various typical scenarios. We apply this framework to analyze several empirical multilayer networks, finding that it can quantify the influences among layers and describe the structure-function relationship of multilayer networks very well.
💲 Open postdoc position to study how information spreads in online social networks. Help spread the word far & wide!
http://cnets.indiana.edu/blog/2017/08/30/socialsim-postdoc/
http://cnets.indiana.edu/blog/2017/08/30/socialsim-postdoc/
Winter School: Sex Hormones and the Brain
Matariki Winter School and Symposium 2018http://www.cin.uni-tuebingen.de/news-events/browse-all-events/detail/view/147/page/1/winter-school-sex-hormones-and-the-brain.html
Matariki Winter School and Symposium 2018http://www.cin.uni-tuebingen.de/news-events/browse-all-events/detail/view/147/page/1/winter-school-sex-hormones-and-the-brain.html
www.cin.uni-tuebingen.de
Detail - CIN Tübingen
The Werner Reichardt Centre for Neuroscience is a Cluster of Excellence focusing upon neurosciences, established at the University of Tübingen in the framework of the Excellence Initiatives funded by the German federal and state governments.
😋 Networks in breakfast
http://www.networkpages.nl/networks-in-breakfast/
http://www.networkpages.nl/networks-in-breakfast/
Complex Systems Studies
🎞 https://youtu.be/uvEUNSu63E4
آپارات
SFI Community Event - Geoffrey West
The Future of the Planet: Life, Growth and Death in Organisms, Cities and Companies, with Geoffrey West
Why do we stop growing, live for 100 years, and sleep eight hours a day? Why do all companies and people die, whereas cities keep growing and the pace…
Why do we stop growing, live for 100 years, and sleep eight hours a day? Why do all companies and people die, whereas cities keep growing and the pace…
🌀 The International Network of Weapon Trade
https://cns.ceu.edu/article/2017-09-01/international-network-weapon-trade
https://cns.ceu.edu/article/2017-09-01/international-network-weapon-trade