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Towards a Methodology for Validation of Centrality Measures in Complex Networks

Towards a Methodology for Validation of Centrality Measures in Complex Networks | Complex Insight  - Understanding our world | Scoop.it

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

 

Batool K, Niazi MA (2014) Towards a Methodology for Validation of Centrality Measures in Complex Networks. PLoS ONE 9(4): e90283. http://dx.doi.org/10.1371/journal.pone.0090283


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Liz Rykert's curator insight, April 15, 2014 10:50 PM

Love this stuff.

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'Golden age' of antibiotics 'set to end'

'Golden age' of antibiotics 'set to end' | Complex Insight  - Understanding our world | Scoop.it

We cannot say we weren't warned. The growing threat of antibiotic resistant organisms is once again in the spotlight. Prof Jeremy Farrar, the new head of Britain's biggest medical research charity the Wellcome Trust said it was a "truly global issue". In his first major interview since taking up his post, Prof Farrar told BBC Radio 4's Today programme that the golden age of antibiotics could come to an end unless action is taken

ComplexInsight's insight:

Prof. Farrar's comments echo those of England's Chief Medical Officer - Sally Davies and the US's Center for Disease Control. As the BBC report shows - warnings regarding the state of anti-biotic effectiveness and bacterial resistance and potential impact started occuring in government circles in the mid 1990's. The World Health Assembly of the WHO will discuss the issue in May 2014. 

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Telecommunications data show civic dividing lines in major countries

Telecommunications data show civic dividing lines in major countries | Complex Insight  - Understanding our world | Scoop.it

Many residents of Britain, Italy, and Belgium imagine there to be a kind of north-south divide in their countries, marking a barrier between different social groups and regional characteristics. Now a new study by MIT researchers reveals that such divides can be seen in the patterns of communication in those countries and others.

 


Via Claudia Mihai, Complexity Institute
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Robustness of skeletons and salient features in networks

Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.

 

Robustness of skeletons and salient features in networks
Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann

http://arxiv.org/abs/1309.3797


Via Complexity Digest, Eugene Ch'ng
ComplexInsight's insight:

 Very relevent to some current work  we are doing on data modeling and data mining -  Awesome scoop  - big thanks Eugene and Complexity Digest..

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What Do Ants Know That We Don't? | Wired Opinion | Wired.com

What Do Ants Know That We Don't? | Wired Opinion | Wired.com | Complex Insight  - Understanding our world | Scoop.it
Ever notice how ant colonies so successfully explore food at 4th of July picnics? It’s all done without any central control.
ComplexInsight's insight:

Biomorphic design - where we take design inspiration from nature is a growing trend in multiple disciplines from architecture and materials science through to network technologies. Ant communication is a well studied - though not necessarily widely understood communication network and like many natural evolved solutions offers many subtle solutions to problems we are only beginning to comprehend.  A nice light introductory article -by Deborah Gordon.

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Flow motifs reveal limitations of the static framework to represent human interactions

Networks are commonly used to define underlying interaction structures where infections, information, or other quantities may spread. Although the standard approach has been to aggregate all links into a static structure, some studies have shown that the time order in which the links are established may alter the dynamics of spreading. In this paper, we study the impact of the time ordering in the limits of flow on various empirical temporal networks. By using a random walk dynamics, we estimate the flow on links and convert the original undirected network (temporal and static) into a directed flow network. We then introduce the concept of flow motifs and quantify the divergence in the representativity of motifs when using the temporal and static frameworks. We find that the regularity of contacts and persistence of vertices (common in email communication and face-to-face interactions) result on little differences in the limits of flow for both frameworks. On the other hand, in the case of communication within a dating site and of a sexual network, the flow between vertices changes significantly in the temporal framework such that the static approximation poorly represents the structure of contacts. We have also observed that cliques with 3 and 4 vertices containing only low-flow links are more represented than the same cliques with all high-flow links. The representativity of these low-flow cliques is higher in the temporal framework. Our results suggest that the flow between vertices connected in cliques depend on the topological context in which they are placed and in the time sequence in which the links are established. The structure of the clique alone does not completely characterize the potential of flow between the vertices.

 

Flow motifs reveal limitations of the static framework to represent human interactions

Luis E. C. Rocha and Vincent D. Blondel 

Phys. Rev. E 87, 042814 (2013)

http://dx.doi.org/10.1103/PhysRevE.87.042814


Via Complexity Digest
ComplexInsight's insight:

In our own research temporality of relationships or interactions between agents is a common property of various systems (think infection, traffic, economic exchange). Maybe its coming from a computer graphics background but their use of Flow motifs reminds me a lot of flow fields and   glyph representations in scientific visualizations.

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Financial Networks and Cartography

Talk at Understanding Financial Catastrophe Risk: Developing a Research Agenda.
ComplexInsight's insight:

Good introduction talk by Kimmo Soramaki on understanding financial risk and types of tools for network analysis. Worth a quick look.

 
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Space net used to control robot

Space net used to control robot | Complex Insight  - Understanding our world | Scoop.it

DTNP is go... Astronauts on the International Space Station use an experimental version of interplanetary internet (more correctly the Disruption Tolerant Networking (DTN) Protocol)  to control a robot on Earth. Vint Cerf proposed this the basis of DTN over 10 years ago and the demonstration shows how communciations using the store and forward protocol are increasingly possible over very large distances. (a previous test was conducted with image transmition between earth and a remote satellite, 20 million miles away.).  Click on the image or title to learn more via the BBC Technologiy article. 

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A simple model clarifies the complicated relationships of complex networks

Researchers have discovered many types of complex networks and have proposed hundreds of models to explain their origins, yet most of the relationships within each of these types are still uncertain. Furthermore, because of the large number of types and models of complex networks, it is widely thought that these complex networks cannot all share a simple universal explanation. However, here we find that a simple model can produce many types of complex networks, including scale-free, small-world, ultra small-world, Delta-distribution, compact, fractal, regular and random networks, and by revising this model, we show that one can produce community-structure networks. Using this model and its revised versions, the complicated relationships among complex networks can be illustrated. Given that complex networks are regarded as a model tool of complex systems, the results here bring a new perspective to understanding the power law phenomena observed in various complex systems.

 

A simple model clarifies the complicated relationships of complex networks

Bojin Zheng, Hongrun Wu, Jun Qin, Wenhua Du, Jianmin Wang, Deyi Li

http://arxiv.org/abs/1210.3121


Via Complexity Digest, Eugene Ch'ng
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New Algorithm Dramatically Streamlines Max Flow Solutions

New Algorithm Dramatically Streamlines Max Flow Solutions | Complex Insight  - Understanding our world | Scoop.it

 Finding the most efficient way to transport items across a network like the U.S. highway system or the Internet is a problem that has taxed mathematicians and computer scientists for decades.

To tackle the problem, researchers have traditionally used a maximum-flow algorithm, also known as “max flow,” in which a network is represented as a graph with a series of nodes, known as vertices, and connecting lines between them, called edges.

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Mathematical Formulation of Multilayer Networks

Mathematical Formulation of Multilayer Networks | Complex Insight  - Understanding our world | Scoop.it

Describing a social network based on a particular type of human social interaction, say, Facebook, is conceptually simple: a set of nodes representing the people involved in such a network, linked by their Facebook connections. But, what kind of network structure would one have if all modes of social interactions between the same people are taken into account and if one mode of interaction can influence another? Here, the notion of a “multiplex” network becomes necessary. Indeed, the scientific interest in multiplex networks has recently seen a surge. However, a fundamental scientific language that can be used consistently and broadly across the many disciplines that are involved in complex systems research was still missing. This absence is a major obstacle to further progress in this topical area of current interest. In this paper, we develop such a language, employing the concept of tensors that is widely used to describe a multitude of degrees of freedom associated with a single entity.

Our tensorial formalism provides a unified framework that makes it possible to describe both traditional “monoplex” (i.e., single-type links) and multiplex networks. Each type of interaction between the nodes is described by a single-layer network. The different modes of interaction are then described by different layers of networks. But, a node from one layer can be linked to another node in any other layer, leading to “cross talks” between the layers. High-dimensional tensors naturally capture such multidimensional patterns of connectivity. Having first developed a rigorous tensorial definition of such multilayer structures, we have also used it to generalize the many important diagnostic concepts previously known only to traditional monoplex networks, including degree centrality, clustering coefficients, and modularity.

We think that the conceptual simplicity and the fundamental rigor of our formalism will power the further development of our understanding of multiplex networks.

 


Via Claudia Mihai, Complexity Institute
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Efficient discovery of overlapping communities in massive networks

Efficient discovery of overlapping communities in massive networks | Complex Insight  - Understanding our world | Scoop.it

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.


Via Claudia Mihai, Complexity Digest, Frédéric Amblard
ComplexInsight's insight:

Network visualization tools like Gephi and analysis tools like SNAP are becoming essential components in understanding, mapping and comprehending inter-relating networks and network processes. This is a good paper that gives insight into appliying networking analysis tools to identify otherwise hidden community structures in apparhently disconnected or partially connected sets which will be hugely important in large scale network analysis.

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Investors Europe Stock Brokers's curator insight, September 1, 2014 2:14 AM

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Markov Network Brains

Markov Network Brains | Complex Insight  - Understanding our world | Scoop.it
In a general sense, a Markov Network Brain (MNB) implements a probabilistic finite state machine, and as such is a Hidden Markov Model (HMM). MNBs act as controllers and decision makers for agents ...
ComplexInsight's insight:

If like me, you are old enough to remember the animals to animats proceedings from the early 1990's which detailed early researchon  agent based modeling, reinforcement learning algorithms and autonomous robots using neural networks, genetic algorithms and other probabilistic finite state machines as control architectures this will be of interest. If you are not -try and find a copy and read up - since a lot of current research is based on early ideas presented in those proceedings. The Adamilab have produced a stable implementation and platform for hidden markov model based controllers for agent based models and robotics. Code is available on Github and the Markov Network Brains article gives a good overview of why its of interest and underlying reasoning behind the implementation for anyone working on agent based simulation and autonomous robot and sensor platforms.

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Networks in Cognitive Science

Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences.

 

Networks in Cognitive Science

Andrea Baronchelli, Ramon Ferrer-i-Cancho, Romualdo Pastor-Satorras, Nick Chater, Morten H. Christiansen

http://arxiv.org/abs/1304.6736


Via Complexity Digest
ComplexInsight's insight:

 Network and complex systems theory are becoming key cornerstones to many fields, and this paper helps explain the mapping to cognitive sciences. Worth a read.

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Jim Price's curator insight, May 24, 2013 12:56 PM

A reminder that complex systems theory is all about scalability (fractals for instance) and that the ways of working of the brain in cognitive science can offer clincial teachers lessons about how we teach in other contexts  - both the classroom & workplace. Just 'think' about it...!

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Robust detection of dynamic community structure in networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (“optimization variance”) and over randomizations of network structure (“randomization variance”). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.

 

Robust detection of dynamic community structure in networks
Danielle S. Bassett, Mason A. Porter, Nicholas F. Wymbs, Scott T. Grafton, Jean M. Carlson, and Peter J. Mucha

Chaos 23, 013142 (2013); http://dx.doi.org/10.1063/1.4790830 ;


Via Complexity Digest, Eugene Ch'ng
ComplexInsight's insight:

Good catch by Eugene and the Complexity Digest team. Ability to examine network structure communal and adhoc in time-dependent networks will become increasingly important with complex systems analysis. Interesting read.

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Complex social contagion makes networks more vulnerable to disease outbreaks

Social network analysis is now widely used to investigate the dynamics of infectious disease spread from person to person. Vaccination dramatically disrupts the disease transmission process on a contact network, and indeed, sufficiently high vaccination rates can disrupt the process to such an extent that disease transmission on the network is effectively halted. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread through social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model both the health behavior and the infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This variability in dynamics results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.

 

Complex social contagion makes networks more vulnerable to disease outbreaks

Ellsworth Campbell, Marcel Salathé

http://arxiv.org/abs/1211.0518


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Thermodynamics of Prediction

A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system’s state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.

 

Thermodynamics of Prediction

Susanne Still, David A. Sivak, Anthony J. Bell, and Gavin E. Crooks

Phys. Rev. Lett. 109, 120604 (2012) 

http://dx.doi.org/10.1103/PhysRevLett.109.120604


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Animating the Web of Life and the Power of Networks

Big fan of RSA Animate and given we deal wtih networked systems everyday this episode was just awesome.  In this RSA Animate, Manuel Lima senior UX design lead at Microsoft Bing, explores the power of network visualisation to help navigate our complex modern world. Click on the image or the title to learn more.


Via FastTFriend, Complexity Institute, Eugene Ch'ng
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