📄 Trade-offs between driving nodes and
time-to-control in complex networks
Sergio Pequito, Victor M. Preciado, #Barabasi , George J. Pappas
🔗 https://pdfs.semanticscholar.org/9217/cb81f364d6a5bad0f70d3c905ba49e6f4e5a.pdf
📌ABSTRACT
We first review some concepts from control theory , graph theory, and structural systems theory. We also include some notions of computational complexity needed in our analysis.
time-to-control in complex networks
Sergio Pequito, Victor M. Preciado, #Barabasi , George J. Pappas
🔗 https://pdfs.semanticscholar.org/9217/cb81f364d6a5bad0f70d3c905ba49e6f4e5a.pdf
📌ABSTRACT
We first review some concepts from control theory , graph theory, and structural systems theory. We also include some notions of computational complexity needed in our analysis.
📄 Experimental econophysics: Complexity, selforganization, and emergent properties
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
http://www.biophysics.org/2017taiwan/Home/tabid/6881/Default.aspx
Single-Cell Biophysics: Measurement, Modulation, and Modeling
Taipei, Taiwan| June 17-20, 2017
Single-Cell Biophysics: Measurement, Modulation, and Modeling
Taipei, Taiwan| June 17-20, 2017
🗞 The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness
Cristopher Moore
🔗 https://arxiv.org/pdf/1702.00467v1
📌 A B S T R A C T
Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is currently motivated by social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a planted "ground truth" community structure around which the graph is generated probabilistically. Our task is then to recover the ground truth knowing only the graph.
Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to be exponentially hard.
For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.
Cristopher Moore
🔗 https://arxiv.org/pdf/1702.00467v1
📌 A B S T R A C T
Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is currently motivated by social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a planted "ground truth" community structure around which the graph is generated probabilistically. Our task is then to recover the ground truth knowing only the graph.
Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to be exponentially hard.
For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.
🗞 Disease Localization in Multilayer Networks
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
🔗 http://journals.aps.org/prx/pdf/10.1103/PhysRevX.7.011014
📌 A B S T R A C T
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, and Yamir Moreno
🔗 http://journals.aps.org/prx/pdf/10.1103/PhysRevX.7.011014
📌 A B S T R A C T
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.
🗞 Takeover times for a simple model of network infection
Bertrand Ottino-Löffler, Jacob G. Scott, Steven H. #Strogatz
🔗 https://arxiv.org/pdf/1702.00881
📌ABSTRACT
We study a stochastic model of infection spreading on a network. At each time step a node is chosen at random, along with one of its neighbors. If the node is infected and the neighbor is susceptible, the neighbor becomes infected. How many time steps T does it take to completely infect a network of Nnodes, starting from a single infected node? An analogy to the classic "coupon collector" problem of probability theory reveals that the takeover time T is dominated by extremal behavior, either when there are only a few infected nodes near the start of the process or a few susceptible nodes near the end. We show that for N≫1, the takeover time T is distributed as a Gumbel for the star graph; as the sum of two Gumbels for a complete graph and an Erd\H{o}s-R\'{e}nyi random graph; as a normal for a one-dimensional ring and a two-dimensional lattice; and as a family of intermediate skewed distributions for d-dimensional lattices with d≥3 (these distributions approach the sum of two Gumbels as d approaches infinity). Connections to evolutionary dynamics, cancer, incubation periods of infectious diseases, first-passage percolation, and other spreading phenomena in biology and physics are discussed.
Bertrand Ottino-Löffler, Jacob G. Scott, Steven H. #Strogatz
🔗 https://arxiv.org/pdf/1702.00881
📌ABSTRACT
We study a stochastic model of infection spreading on a network. At each time step a node is chosen at random, along with one of its neighbors. If the node is infected and the neighbor is susceptible, the neighbor becomes infected. How many time steps T does it take to completely infect a network of Nnodes, starting from a single infected node? An analogy to the classic "coupon collector" problem of probability theory reveals that the takeover time T is dominated by extremal behavior, either when there are only a few infected nodes near the start of the process or a few susceptible nodes near the end. We show that for N≫1, the takeover time T is distributed as a Gumbel for the star graph; as the sum of two Gumbels for a complete graph and an Erd\H{o}s-R\'{e}nyi random graph; as a normal for a one-dimensional ring and a two-dimensional lattice; and as a family of intermediate skewed distributions for d-dimensional lattices with d≥3 (these distributions approach the sum of two Gumbels as d approaches infinity). Connections to evolutionary dynamics, cancer, incubation periods of infectious diseases, first-passage percolation, and other spreading phenomena in biology and physics are discussed.
⭕️ First International Summer Institute
on Network Physiology (ISINP)
Lake Como School of Advanced Studies – July 24-29, 2017
http://isinp.lakecomoschool.org/application-form/
on Network Physiology (ISINP)
Lake Como School of Advanced Studies – July 24-29, 2017
http://isinp.lakecomoschool.org/application-form/
🗞 Random Spatial Networks: Small Worlds without Clustering, Traveling Waves, and Hop-and-Spread Disease Dynamics
John Lang, Hans De Sterck, Jamieson L. Kaiser, Joel C. Miller
🔗https://arxiv.org/pdf/1702.01252
📌 ABSTRACT
Random network models play a prominent role in modeling, analyzing and understanding complex phenomena on real-life networks. However, a key property of networks is often neglected: many real-world networks exhibit spatial structure, the tendency of a node to select neighbors with a probability depending on physical distance. Here, we introduce a class of random spatial networks (RSNs) which generalizes many existing random network models but adds spatial structure. In these networks, nodes are placed randomly in space and joined in edges with a probability depending on their distance and their individual expected degrees, in a manner that crucially remains analytically tractable. We use this network class to propose a new generalization of small-world networks, where the average shortest path lengths in the graph are small, as in classical Watts-Strogatz small-world networks, but with close spatial proximity of nodes that are neighbors in the network playing the role of large clustering. Small-world effects are demonstrated on these spatial small-world networks without clustering. We are able to derive partial integro-differential equations governing susceptible-infectious-recovered disease spreading through an RSN, and we demonstrate the existence of traveling wave solutions. If the distance kernel governing edge placement decays slower than exponential, the population-scale dynamics are dominated by long-range hops followed by local spread of traveling waves. This provides a theoretical modeling framework for recent observations of how epidemics like Ebola evolve in modern connected societies, with long-range connections seeding new focal points from which the epidemic locally spreads in a wavelike manner.
John Lang, Hans De Sterck, Jamieson L. Kaiser, Joel C. Miller
🔗https://arxiv.org/pdf/1702.01252
📌 ABSTRACT
Random network models play a prominent role in modeling, analyzing and understanding complex phenomena on real-life networks. However, a key property of networks is often neglected: many real-world networks exhibit spatial structure, the tendency of a node to select neighbors with a probability depending on physical distance. Here, we introduce a class of random spatial networks (RSNs) which generalizes many existing random network models but adds spatial structure. In these networks, nodes are placed randomly in space and joined in edges with a probability depending on their distance and their individual expected degrees, in a manner that crucially remains analytically tractable. We use this network class to propose a new generalization of small-world networks, where the average shortest path lengths in the graph are small, as in classical Watts-Strogatz small-world networks, but with close spatial proximity of nodes that are neighbors in the network playing the role of large clustering. Small-world effects are demonstrated on these spatial small-world networks without clustering. We are able to derive partial integro-differential equations governing susceptible-infectious-recovered disease spreading through an RSN, and we demonstrate the existence of traveling wave solutions. If the distance kernel governing edge placement decays slower than exponential, the population-scale dynamics are dominated by long-range hops followed by local spread of traveling waves. This provides a theoretical modeling framework for recent observations of how epidemics like Ebola evolve in modern connected societies, with long-range connections seeding new focal points from which the epidemic locally spreads in a wavelike manner.
Forwarded from Brain@IPM
"Hands on Statistics with R; Applied Methods in Cognitive Sciences"
7th – 9th March 2017 (17th – 19th Esfand 1395)
School of Cognitive Sciences, IPM, Larak, Artesh Blvd, Tehran http://scs.ipm.ac.ir/conferences/2017/rworkshop/index.jsp
7th – 9th March 2017 (17th – 19th Esfand 1395)
School of Cognitive Sciences, IPM, Larak, Artesh Blvd, Tehran http://scs.ipm.ac.ir/conferences/2017/rworkshop/index.jsp
📽 How can we understand our complex economy?
https://podcasts.ox.ac.uk/how-can-we-understand-our-complex-economy
Video (1.01 GB)
🔗 http://media.podcasts.ox.ac.uk/maths/oxford-maths/2016-11-03_maths_farmer-720p.mp4?_ga=1.236570369.1241202832.1486574109
https://podcasts.ox.ac.uk/how-can-we-understand-our-complex-economy
Video (1.01 GB)
🔗 http://media.podcasts.ox.ac.uk/maths/oxford-maths/2016-11-03_maths_farmer-720p.mp4?_ga=1.236570369.1241202832.1486574109
podcasts.ox.ac.uk
How can we understand our complex economy? | University of Oxford Podcasts - Audio and Video Lectures
We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on ourselves?
Lectures and seminars organised by the Nuffield Department of Clinical Neurosciences
https://podcasts.ox.ac.uk/series/nuffield-department-clinical-neurosciences
https://podcasts.ox.ac.uk/series/nuffield-department-clinical-neurosciences