Simplifying Complexity
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Simplifying Complexity
ecology and complexity science
Curated by Eric L Berlow
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Network deconvolution as a general method to distinguish direct dependencies in networks

Network deconvolution as a general method to distinguish direct dependencies in networks | Simplifying Complexity | Scoop.it

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.

 

Network deconvolution as a general method to distinguish direct dependencies in networks
Soheil Feizi, Daniel Marbach, Muriel Médard & Manolis Kellis

Nature Biotechnology 31, 726–733 (2013) http://dx.doi.org/10.1038/nbt.2635


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Dmitry Alexeev's curator insight, August 13, 2013 12:30 AM

complexity is paving the way - now we look for directed graph

 

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Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity

Food webs are networks of feeding interactions among species. Although parasites comprise a large proportion of species diversity, they have generally been underrepresented in food web data and analyses. Previous analyses of the few datasets that contain parasites have indicated that their inclusion alters network structure. However, it is unclear whether those alterations were a result of unique roles that parasites play, or resulted from the changes in diversity and complexity that would happen when any type of species is added to a food web. In this study, we analyzed many aspects of the network structure of seven highly resolved coastal estuary or marine food webs with parasites. In most cases, we found that including parasites in the analysis results in generic changes to food web structure that would be expected with increased diversity and complexity. However, in terms of specific patterns of links in the food web (“motifs”) and the breadth and contiguity of feeding niches, parasites do appear to alter structure in ways that result from unique traits—in particular, their close physical intimacy with their hosts, their complex life cycles, and their small body sizes. Thus, this study disentangles unique from generic effects of parasites on food web organization, providing better understanding of similarities and differences between parasites and free-living species in their roles as consumers and resources.

 

Dunne JA, Lafferty KD, Dobson AP, Hechinger RF, Kuris AM, et al. (2013) Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity. PLoS Biol 11(6): e1001579. http://dx.doi.org/10.1371/journal.pbio.1001579


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Biology: The big challenges of big data

Biology: The big challenges of big data | Simplifying Complexity | Scoop.it

Biologists are joining the big-data club. With the advent of high-throughput genomics, life scientists are starting to grapple with massive data sets, encountering challenges with handling, processing and moving information that were once the domain of astronomers and high-energy physicists

 

Biology: The big challenges of big data

Vivien Marx
Nature 498, 255–260 (13 June 2013)

http://dx.doi.org/10.1038/498255a


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Big Data Drives 'National Day Of Civic Hacking'

Big Data Drives 'National Day Of Civic Hacking' | Simplifying Complexity | Scoop.it
Nationwide hackathon this weekend encourages coders to use publicly available data to tackle problems ranging from poverty to poultry handling.
Eric L Berlow's insight:

For Intel, the hacking event is the latest in a series of initiatives designed to draw attention to big data's impact on society. WeTheData, a research program the company conducted last year in collaboration with Vibrant Data Labs, a collective of scientists, artists, and designers, posed technical challenges that focused on the democratization of digital information.

The WeTheData research made it clear that data literacy is a fundamental principle that must be in place for a new "data society" to emerge, Barnett noted. Events like the National Day of Civic Hacking are designed to move that process by making hard-to get (and often inaccessible) data sets available to everyone.

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Large-scale global optimization through consensus of opinions over complex networks

Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks.

 

Large-scale global optimization through consensus of opinions over complex networks
Omid Askari Sichani and Mahdi Jalili

Complex Adaptive Systems Modeling 2013, 1:11 http://dx.doi.org/10.1186/2194-3206-1-11


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Employment Growth through Labor Flow Networks

Employment Growth through Labor Flow Networks | Simplifying Complexity | Scoop.it

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

 

Guerrero OA, Axtell RL (2013) Employment Growth through Labor Flow Networks. PLoS ONE 8(5): e60808. http://dx.doi.org/10.1371/journal.pone.0060808


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Visually-Driven Urban Simulation: exploring fast and slow change in residential location

A large-scale residential-location model of the Greater London region is being developed in which all stages of the model-building process—from data input, analysis through calibration to prediction—are rapid to execute and accessible in a visual and immediate fashion. The model is structured to distribute trips across competing modes of transport from employment to population locations. It is cast in an entropy-maximising framework which has been extended to measure actual components of energy—travel costs, free energy, and unusable energy (entropy itself)—and these provide indicators for examining future scenarios based on changing the costs of travel in the metro region. Although the model is comparatively static, we interpret its predictions in terms of fast and slow processes—‘fast’ relating to changes in transport modes, and ‘slow’ relating to changes in location. After developing and explaining the model using appropriate visual analytics, a scenario in which road-travel costs double is tested: this shows that mode switching is considerably more significant than shifts in location—which are minimal. 

 

Batty M, 2013, "Visually-Driven Urban Simulation: exploring fast and slow change in residential location" Environment and Planning A 45(3) 532 – 552 

http://www.envplan.com/abstract.cgi?id=a44153


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Net gains

Physics — and physicists — have had much to contribute to economic and finance. Now the science of complex networks sets a way forward to understanding and managing the complex financial networks of the world's markets.

 

Net gains

Nature Physics 9, 119 (2013) http://dx.doi.org/10.1038/nphys2588
Published online 01 March 2013

 

Focus issue: Complex networks in finance

http://www.nature.com/nphys/focus/finance/index.html


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Noise enhances information transfer in hierarchical networks

Noise enhances information transfer in hierarchical networks | Simplifying Complexity | Scoop.it

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

 

Noise enhances information transfer in hierarchical networks

Agnieszka Czaplicka, Janusz A. Holyst & Peter M. A. Sloot

Scientific Reports 3, Article number: 1223 http://dx.doi.org/10.1038/srep01223


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On Early Warning Signs § SEEDMAGAZINE.COM

On Early Warning Signs § SEEDMAGAZINE.COM | Simplifying Complexity | Scoop.it
Rapid shifts are the hallmark of climate change, epileptic seizures, financial crises, and fishery collapses. Deep mathematical principles tie these events together.
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Eric L Berlow's comment, January 31, 2013 12:43 PM
thanks anna!
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The Origins of Scaling in Cities

Cities are perhaps the ultimate expression of human sociality displaying at once humanity’s greatest achievements and some of its most difficult challenges. Despite the increasing importance of cities in human societies our ability to understand them scientifically, and manage them in practice, has remained unsatisfactorily limited. The greatest difficulties to any scientific approach to cities have resulted from their many interdependent facets, as social, economic, infrastructural and spatial complex systems, which exist in similar but changing forms over a huge range of scales. Here, I show how cities may evolve following a small set of basic principles that operate locally and can explain how cities change gradually from the bottom-up. As a result I obtain a theoretical framework that derives the general open-ended properties of cities through the optimization of a set of local conditions. This framework is used to predict, in a unified and quantitative way, the average social, spatial and infrastructural properties of cities as a set of scaling relations that apply to all urban systems, many of which have been observed in nations around the world. Finally, I compare and contrast the structure and dynamics of cities to those of other complex systems that share some analogous properties.

 

The Origins of Scaling in Cities
Lúis M. A. Bettencourt

SFI-WP 12-09-014


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How Much Does Crowd Funding Cost Musicians? : NPR

Two bands, Los Angeles-based A House For Lions and Maine's The Mallett Brothers, add up what they've spent while asking you for money.

 

Beware the sunk costs of crowdfunding! -ABS


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Universities Scale Like Cities

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the gross university income in terms of total number of citations over size in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling.

 

Universities Scale Like Cities

Anthony F. J. van Raan

http://arxiv.org/abs/1211.5124


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Data-sharing: Everything on display

Data-sharing: Everything on display | Simplifying Complexity | Scoop.it

Some communities have agreed to share online — geneticists, for example, post DNA sequences at the GenBank repository, and astronomers are accustomed to accessing images of galaxies and stars from, say, the Sloan Digital Sky Survey, a telescope that has observed some 500 million objects — but these remain the exception, not the rule. Historically, scientists have objected to sharing for many reasons: it is a lot of work; until recently, good databases did not exist; grant funders were not pushing for sharing; it has been difficult to agree on standards for formatting data and the contextual information called metadata; and there is no agreed way to assign credit for data.
But the barriers are disappearing, in part because journals and funding agencies worldwide are encouraging scientists to make their data public.

 

Data-sharing: Everything on display
Richard Van Noorden
Nature 500, 243–245 (08 August 2013) http://dx.doi.org/10.1038/nj7461-243a


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Eric L Berlow's insight:

Sharing 'Small Data' is a huge challenge - nice to see it finally taking off.

 

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Network Science at Center of Surveillance Dispute

Last week, civil libertarians cried foul when press reports revealed that, in its efforts to ferret out terrorists, the U.S. National Security Agency (NSA) is collecting cell phone records and Internet data from companies such as Verizon, Facebook, and Skype. Some argued that the federal government is spying on its own citizens. From the nature of the data, scientists say it's clear that NSA is performing network analysis, a type of science that aims to identify social groups from the connections among people. And NSA is hardly the only organization doing such work, researchers say. Private companies are already tracing people's social circles.

 

Network Science at Center of Surveillance Dispute
Adrian Cho

Science 14 June 2013:
Vol. 340 no. 6138 p. 1272
http://dx.doi.org/10.1126/science.340.6138.1272


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Luciano Lampi's curator insight, June 15, 2013 7:54 AM

who is doing network analysis with your connections? Do you care?

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Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series

Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series | Simplifying Complexity | Scoop.it

In financial markets, participants locally optimize their profit which can result in a globally unstable state leading to a catastrophic change. The largest crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks due to high-risk trading in complex products. We introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information, and apply it to the time series of USD and EUR interest rate swaps (IRS). We find in both markets that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy, and decreases afterwards. Previously introduced indicators such as ‘critical slowing down’ do not provide a clear leading indicator. Our results suggest that the IDL may be used as an early-warning signal for critical transitions even in the absence of a predictive model.

Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series
Rick Quax, Drona Kandhai & Peter M. A. Sloot
Scientific Reports 3, Article number: 1898
http://dx.doi.org/10.1038/srep01898

 


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Michael Power's comment, June 7, 2013 3:45 PM
Investment bankers will build this into their models and it will become a late-warning sign.
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Global Multi-Level Analysis of the ‘Scientific Food Web' : Scientific Reports : Nature Publishing Group

Global Multi-Level Analysis of the ‘Scientific Food Web' : Scientific Reports : Nature Publishing Group | Simplifying Complexity | Scoop.it
We introduce a network-based index analyzing excess scientific production and consumption to perform a comprehensive global analysis of scholarly knowledge production and diffusion on the level of continents, countries, and cities.

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Globally networked risks and how to respond

Today’s strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking.

 

Globally networked risks and how to respond

Dirk Helbing
Nature 497, 51–59 (02 May 2013) http://dx.doi.org/10.1038/nature12047


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Stephen Hawking’s advice for twenty-first century grads: Embrace complexity

 A few years ago, Hawking was asked what he thought of the common opinion that the twentieth century was that of biology and the twenty-first century would be that of physics. Hawking replied that in his opinion the twenty-first century would be the “century of complexity”. That remark probably holds more useful advice for contemporary students than they realize since it points to at least two skills which are going to be essential for new college grads in the age of complexity: statistics and data visualization.


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Harshal Hayatnagarkar's curator insight, April 25, 2013 2:17 PM
Exactly, Sir !
Dmitry Alexeev's curator insight, April 29, 2013 7:15 AM

Complexity is us)

Murray McKercher's curator insight, April 30, 2013 7:39 AM

"century of complexity" sounds like we should therefore concentrate on simplicity in all things mobile...

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Characterizing scientific production and consumption in Physics

Characterizing scientific production and consumption in Physics | Simplifying Complexity | Scoop.it

We analyze the entire publication database of the American Physical Society generating longitudinal (50 years) citation networks geolocalized at the level of single urban areas. We define the knowledge diffusion proxy, and scientific production ranking algorithms to capture the spatio-temporal dynamics of Physics knowledge worldwide. By using the knowledge diffusion proxy we identify the key cities in the production and consumption of knowledge in Physics as a function of time. The results from the scientific production ranking algorithm allow us to characterize the top cities for scholarly research in Physics. Although we focus on a single dataset concerning a specific field, the methodology presented here opens the path to comparative studies of the dynamics of knowledge across disciplines and research areas.

 

Characterizing scientific production and consumption in Physics

Qian Zhang, Nicola Perra, Bruno Gonçalves, Fabio Ciulla & Alessandro Vespignani

Scientific Reports 3, Article number: 1640 http://dx.doi.org/10.1038/srep01640


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Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields

Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields | Simplifying Complexity | Scoop.it

We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

 

Chavalarias D, Cointet J-P (2013) Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields. PLoS ONE 8(2): e54847. http://dx.doi.org/10.1371/journal.pone.0054847


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Spread of Academic Success in a High School Social Network

Spread of Academic Success in a High School Social Network | Simplifying Complexity | Scoop.it

Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students’ academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends’ average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

 

Blansky D, Kavanaugh C, Boothroyd C, Benson B, Gallagher J, et al. (2013) Spread of Academic Success in a High School Social Network. PLoS ONE 8(2): e55944. http://dx.doi.org/10.1371/journal.pone.0055944

 


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Eric L Berlow's insight:

close network neighborhood of friends, not acquaintances, determines future academic success

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Complexity Digest's curator insight, February 14, 2013 5:13 PM

This research was made mainly by high school students.

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Adapting to a warmer world: No going back

Adapting to a warmer world: No going back | Simplifying Complexity | Scoop.it

Just a decade ago, 'adaptation' was something of a dirty word in the climate arena — an insinuation that nations could continue with business as usual and deal with the mess later. But greenhouse-gas emissions are increasing at an unprecedented rate and countries have failed to negotiate a successor to the Kyoto Protocol climate treaty. That stark reality has forced climate researchers and policy-makers to explore ways to weather some of the inevitable changes.


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The emerging science of 'collective intelligence' — and the rise of the global brain

The emerging science of 'collective intelligence' — and the rise of the global brain | Simplifying Complexity | Scoop.it

Over at the Edge there's a fascinating article by Thomas W. Malone about the work he and others are doing to understand the rise of collective human intelligence — an emergent phenomenon that's being primarily driven by our information technologies. We may be on an evolutionary trajectory, he argues, that could someday give rise to the global brain. And amazingly, he's developing an entirely new scientific discipline to back his case.


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The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested.

 

Bustos S, Gomez C, Hausmann R, Hidalgo CA (2012) The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems. PLoS One 7(11): e49393. http://dx.doi.org/10.1371/journal.pone.0049393


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