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Cross-checking different sources of mobility information

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2x2 and 1x1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.


Cross-checking different sources of mobility information
Maxime Lenormand, Miguel Picornell, Oliva G. Cantu-Ros, Antonia Tugores, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frias-Martinez, Jose J. Ramasco

http://arxiv.org/abs/1404.0333

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Followers Are Not Enough: Beyond Structural Communities in Online Social Networks

Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as "friends" on Facebook and "followers" on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that community detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of 'community' is very general, and different questions such as "who do we interact with?" and "with whom do we share similar interests?" can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights into the network under study.


Followers Are Not Enough: Beyond Structural Communities in Online Social Networks
David Darmon, Elisa Omodei, Joshua Garland

http://arxiv.org/abs/1404.0300

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The Informative Herd: why humans and other animals imitate more when conditions are adverse

Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option.


The Informative Herd: why humans and other animals imitate more when conditions are adverse
Alfonso Pérez-Escudero, Gonzalo G. de Polavieja

http://arxiv.org/abs/1403.7478

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António F Fonseca's curator insight, April 4, 2014 5:02 AM

I believe logic emerges from imitation.

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A solution to the collective action problem in between-group conflict with within-group inequality

Conflict with conspecifics from neighbouring groups over territory, mating opportunities and other resources is observed in many social organisms, including humans. Here we investigate the evolutionary origins of social instincts, as shaped by selection resulting from between-group conflict in the presence of a collective action problem. We focus on the effects of the differences between individuals on the evolutionary dynamics. Our theoretical models predict that high-rank individuals, who are able to usurp a disproportional share of resources in within-group interactions, will act seemingly altruistically in between-group conflict, expending more effort and often having lower reproductive success than their low-rank group-mates. Similar behaviour is expected for individuals with higher motivation, higher strengths or lower costs, or for individuals in a leadership position. Our theory also provides an evolutionary foundation for classical equity theory, and it has implications for the origin of coercive leadership and for reproductive skew theory.


A solution to the collective action problem in between-group conflict with within-group inequality
• Sergey Gavrilets & Laura Fortunato

Nature Communications 5, Article number: 3526 http://dx.doi.org/10.1038/ncomms4526 

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Modeling social-ecological problems in coastal ecosystems: A case study

Complex social-ecological systems (SES) are not amenable to simple mathematical modeling. However, to address critical issues in SES (e.g., understanding ecological resilience/amelioration of poverty) it is necessary to describe such systems in their entirety. Based on empirical knowledge of local stakeholders and experts, we mapped their conceptions of one SES. Modelers codified what actors told us into two models: a local-level model and an overarching multiple-entity description of the system. Looking at these two representations together helps us understand links between the locally specific and other levels of decision taking and vice-versa. This “bimodeling” approach is investigated in one SES in coastal Kenya.



Modeling social-ecological problems in coastal ecosystems: A case study
John Forrester, Richard Greaves, Howard Noble and Richard Taylor
Complexity

http://dx.doi.org/10.1002/cplx.21524

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Predicting Successful Memes using Network and Community Structure

Predicting Successful Memes using Network and Community Structure | Papers | Scoop.it
We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
-- To be presented at ICWSM 2014
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The Simple Rules of Social Contagion

It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.


The Simple Rules of Social Contagion
Nathan O. Hodas & Kristina Lerman

Scientific Reports 4, Article number: 4343 http://dx.doi.org/10.1038/srep04343


Via Shaolin Tan, NESS
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The Ecology of Collective Behavior

Similar patterns of interaction, such as network motifs and feedback loops, are used in many natural collective processes, probably because they have evolved independently under similar pressures. Here I consider how three environmental constraints may shape the evolution of collective behavior: the patchiness of resources, the operating costs of maintaining the interaction network that produces collective behavior, and the threat of rupture of the network. The ants are a large and successful taxon that have evolved in very diverse environments. Examples from ants provide a starting point for examining more generally the fit between the particular pattern of interaction that regulates activity, and the environment in which it functions.


Gordon DM (2014) The Ecology of Collective Behavior. PLoS Biol 12(3): e1001805. http://dx.doi.org/10.1371/journal.pbio.1001805


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Technology: The $1,000 genome

Technology: The $1,000 genome | Papers | Scoop.it

With a unique programme, the US government has managed to drive the cost of genome sequencing down towards a much-anticipated target.

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Complex networks - EPL

Complex networks - EPL | Papers | Scoop.it

To highlight the excellent quality of this research, this is an open compilation of the most downloaded articles published in EPL during the past few years.


http://iopscience.iop.org/0295-5075/page/Complex%20networks

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Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

It is common in the study of networks to investigate meso-scale features to try to understand network structure and function. For example, numerous algorithms have been developed to try to identify ``communities,'' which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that ``communities'' are associated with bottlenecks of dynamical processes that begin at locally-biased seed sets of nodes, and we employ several different community-identification procedures to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for ``size-resolved community structure'' that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. 


Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks
Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney

http://arxiv.org/abs/1403.3795

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From Drivers to Athletes -- Modeling and Simulating Cross-Country Sking Marathons

Traffic flow of athletes in classic-style cross-country ski marathons, with the Swedish Vasaloppet as prominent example, represents a non-vehicular system of driven particles with many properties of vehicular traffic flow such as unidirectional movement, the existence of lanes, and, moreover, severe traffic jams. We propose a microscopic acceleration and track-changing model taking into account different fitness levels, gradients, and interactions between the athletes in all traffic situations. The model is calibrated on microscopic data of the Vasaloppet 2012. Using the multi-model open-source simulator MovSim.org, we simulate all 15 000 participants of the Vasaloppet during the first ten kilometers.


From Drivers to Athletes -- Modeling and Simulating Cross-Country Sking Marathons
Martin Treiber, Ralph Germ, Arne Kesting

http://arxiv.org/abs/1403.4965


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Crowd Flow Modeling of Athletes in Mass Sports Events -- a Macroscopic Approach
Martin Treiber

http://arxiv.org/abs/1403.4969 

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Local active information storage as a tool to understand distributed neural information processing

Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today’s digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, such definitions were given and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.

 

Michael Wibral, Joseph T. Lizier, Sebastian Vögler, Viola Priesemann and Ralf Galuske,

Local active information storage as a tool to understand distributed neural information processing

Frontiers in Neuroinformatics 8:1 (2014)

http://dx.doi.org/10.3389/fninf.2014.00001 

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Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data

The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games. In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.


Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data
Petko Georgiev, Anastasios Noulas, Cecilia Mascolo

http://arxiv.org/abs/1403.7654


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The Call of the Crowd: Event Participation in Location-based Social Services
Petko Georgiev, Anastasios Noulas, Cecilia Mascolo

http://arxiv.org/abs/1403.7657 

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You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare

Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users' cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions.


You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare
Thiago H Silva, Pedro O S Vaz de Melo, Jussara Almeida, Mirco Musolesi, Antonio Loureiro

http://arxiv.org/abs/1404.1009

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Emergence of multicellularity in a model of cell growth, death and aggregation under size-dependent selection

How multicellular life forms evolved out from unicellular ones constitutes a major problem in our understanding of the evolution of our biosphere. A recent set of experiments involving yeast cell populations has shown that selection for larger aggregates leads to the appearance of stable clusters of cells that are able to split into smaller aggregates. It was suggested that the observed evolutionary patterns could be the result of evolved programs affecting cell death. Here we show, using a simple model of cell-cell interactions and evolving adhesion rates, that the observed patterns in cluster size and localized mortality can be easily interpreted in terms of diffusion-limited growth dynamics. An experimental test of this scenario is proposed. This simple mechanism would have played a key role in the early evolution of multicellular life forms based on aggregative development. The potential extensions of this work and its implications for natural and synthetic multicellularity are discussed.


Emergence of multicellularity in a model of cell growth, death and aggregation under size-dependent selection
Salva Duran-Nebreda, Ricard V. Solé

http://arxiv.org/abs/1404.0196

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Complexity in Animal Communication: Estimating the Size of N-Gram Structures

In this paper, new techniques that allow conditional entropy to estimate the combinatorics of symbols are applied to animal communication studies to estimate the communication’s repertoire size. By using the conditional entropy estimates at multiple orders, the paper estimates the total repertoire sizes for animal communication across bottlenose dolphins, humpback whales and several species of birds for an N-gram length of one to three. In addition to discussing the impact of this method on studies of animal communication complexity, the reliability of these estimates is compared to other methods through simulation. While entropy does undercount the total repertoire size due to rare N-grams, it gives a more accurate picture of the most frequently used repertoire than just repertoire size alone.


Complexity in Animal Communication: Estimating the Size of N-Gram Structures
Reginald Smith

Entropy 2014, 16(1), 526-542; http://dx.doi.org/10.3390/e16010526


Help fund the open access fee!
https://www.kickstarter.com/projects/683516221/dolphin-and-whale-language-research-paper-funding

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Sketching a network portrait of the humber region

Industrial systems can be represented as networks of organizations connected by flows of materials, energy, and money. This network context may produce unexpected consequences in response to policy intervention, so improved understanding is vital; however, industrial network data are commonly unavailable publically. Using a case study in the Humber region, UK, we present a novel methodology of “network coding” of semistructured interviews with key industrial and political stakeholders, in combination with an “industrial taxonomy” of network archetypes developed to construct an approximation of the region's networks when data are incomplete. This article describes our methodology and presents the resulting network.



Sketching a network portrait of the humber region
Alexandra S. Penn, Paul D. Jensen, Amy Woodward, Lauren Basson, Frank Schiller and Angela Druckman

Complexity

http://dx.doi.org/10.1002/cplx.21519

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Neural signatures of betrayal aversion: an fMRI study of trust

Decisions are said to be ‘risky’ when they are made in environments with uncertainty caused by nature. By contrast, a decision is said to be ‘trusting’ when its outcome depends on the uncertain decisions of another person. A rapidly expanding literature reveals economically important differences between risky and trusting decisions, and further suggests these differences are due to ‘betrayal aversion’.

[...] Here, we provide evidence from an fMRI study that supports this hypothesis.

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Swarming in Biological and Related Systems

Swarming in Biological and Related Systems | Papers | Scoop.it

In the last 15 years, the collective motion of large numbers of self-propelled objects has become an increasingly active area of research. The examples of such collective motion abound: flocks of birds, schools of fish, swarms of insects, herds of animals etc. Swarming of living creatures is believed to be critical for the population survival under harsh conditions. The ability of motile microorganisms to communicate and coordinate their motion leads to the remarkably complex self-organized structures found in bacterial biofilms. Active intracellular transport of biological molecules within the cytoskeleton has a profound effect on the cell cycle, signaling and motility. In recent years, significant progress has also been achieved in the design of synthetic self-propelled particles. Their collective motion has many advantages for performing specific robotic tasks, such as collective cargo delivery or harvesting the mechanical energy of chaotic motion.

(...)

In this focus issue we have tried to assemble papers from leading experts which we hope will provide a current snapshot of this young and rapidly expanding field of research. They cover both theoretical and experimental investigations of the dynamics of active matter on different spatial and temporal scales.


Focus on Swarming in Biological and Related Systems
Lev Tsimring, Hugues Chate, 
Igor Aronson

2014 New J. Phys. 16

http://iopscience.iop.org/1367-2630/focus/Focus%20on%20Swarming%20in%20Biological%20and%20Related%20Systems

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Control Profiles of Complex Networks

Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.


Control Profiles of Complex Networks
Justin Ruths, Derek Ruths

Science 21 March 2014:
Vol. 343 no. 6177 pp. 1373-1376
http://dx.doi.org/10.1126/science.1242063

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Networking—a statistical physics perspective

Networking—a statistical physics perspective | Papers | Scoop.it

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.


Networking—a statistical physics perspective

Chi Ho Yeung and David Saad 2013 J. Phys. A: Math. Theor. 46 103001

http://dx.doi.org/10.1088/1751-8113/46/10/103001

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The Web of Life

The Web of Life (www.web-of-life.es) provides a graphical user interface, based on Google Maps, for easily visualizing and downloading data on ecological networks of species interactions. It is designed and implemented in a relational database management system, allowing sophisticated and user-friendly searching across networks. Users can access the database by any web browser using a variety of operating systems. Data can be downloaded in several common formats, and a data transmission webservice in JavaScript Object Notation is also provided.


The Web of Life
Miguel A. Fortuna, Raul Ortega, Jordi Bascompte

http://arxiv.org/abs/1403.2575

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Eli Levine's curator insight, March 21, 2014 6:43 PM

Way cool stuff.

 

And we're, so far, proving toxic to the whole thing, including to ourselves, through our economic and social activity.

 

A shame, really, because it is only for monetary profits that we're prioritizing our efforts, when it takes so much more to create a happy, healthy existence for ourselves on this planet or, indeed, on any planet we may one day colonize.

 

Think about it.

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General Centrality in a hypergraph

The goal of this paper is to present a centrality measurement for the nodes of a hypergraph, by using existing literature which extends eigenvector centrality from a graph to a hypergraph, and literature which give a general centrality measurement for a graph. We will use this measurement to say more about the number of communications in a hypergraph, to implement a learning mechanism, and to construct certain networks.


General Centrality in a hypergraph
Evo Busseniers

http://arxiv.org/abs/1403.5162

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Optimal network clustering for information diffusion

We investigate the impact of community structure on information spreading with the linear threshold model. Contrary to the common belief that communities hinder information diffusion, we show that strong communities can facilitate global cascades by enhancing local, intra-community spreading. Using both analytical approaches and numerical simulations, we demonstrate the existence of optimal clustering, where global cascades require the minimal number of early adopters.

 

Azadeh Nematzadeh, Emilio Ferrara, Alessandro Flammini, Yong-Yeol Ahn

"Optimal network clustering for information diffusion"

arXiv:1401.1257

http://arxiv.org/abs/1401.1257

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june holley's curator insight, May 3, 2014 7:40 AM

Basically states that what we call a Smart Network (loosely connected clusters) is optimal for incubating new ideas and then spreading them.

Stephen Dale's curator insight, May 6, 2014 6:59 AM

Research paper on optimal network structures for information diffusion.