Networks and Graphs
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Algorithm Writes People's Life Histories Using Twitter

Algorithm Writes People's Life Histories Using Twitter | Networks and Graphs | Scoop.it

“If you tweet about your life, a new algorithm can identify your most significant events and assemble them into an accurate life history, say the computer scientists who built it (Algorithm Writes People's Life Histories Using Twitter”


Via Claudia Mihai
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luiy's curator insight, October 9, 2013 6:48 AM

Twitter allows anyone to describe their life in unprecedented detail. Many accounts provide an ongoing commentary of an individual’s interests, activities and opinions. 


So it’s not hard to imagine that it’s possible to reconstruct a person’s life history by analysing their Twitter stream.

But doing this automatically is trickier than it sounds. That’s because most Twitter streams contain news of important events mixed up with entirely trivial details about events of little or no significance. The difficulty is in telling these apart.


Ref:arxiv.org/abs/1309.7313 : Timeline Generation: Tracking individuals on Twitter

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Network Science by Albert-László Barabási

Network Science by Albert-László Barabási | Networks and Graphs | Scoop.it

Network Science, a textbook for network science, is freely available under the Creative Commons licence. Follow its development onFacebook, Twitter or by signining up to our mailing list, so that we can notify you of new chapters and developments.

The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella,Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2015. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.

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Population-Area Relationship for Medieval European Cities

Population-Area Relationship for Medieval European Cities | Networks and Graphs | Scoop.it
Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems. Yet, many of the spatial, political and economic features of medieval European cities were particular to the Middle Ages, and subsequently changed over the Early Modern Period and Industrial Revolution. There is a long tradition of demographic studies estimating the population sizes of medieval European cities, and comparative analyses of these data have shed much light on the long-term evolution of urban systems. However, the next step—to systematically relate the population size of these cities to their spatial and socioeconomic characteristics—has seldom been taken. This raises a series of interesting questions, as both modern and ancient cities have been observed to obey area-population relationships predicted by settlement scaling theory. To address these questions, we analyze a new dataset for the settled area and population of 173 European cities from the early fourteenth century to determine the relationship between population and settled area. To interpret this data, we develop two related models that lead to differing predictions regarding the quantitative form of the population-area relationship, depending on the level of social mixing present in these cities. Our empirical estimates of model parameters show a strong densification of cities with city population size, consistent with patterns in contemporary cities. Although social life in medieval Europe was orchestrated by hierarchical institutions (e.g., guilds, church, municipal organizations), our results show no statistically significant influence of these institutions on agglomeration effects. The similarities between the empirical patterns of settlement relating area to population observed here support the hypothesis that cities throughout history share common principles of organization that self-consistently relate their socioeconomic networks to structured urban spaces.
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Flavor network and the principles of food pairing

Flavor network and the principles of food pairing | Networks and Graphs | Scoop.it

The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.

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Rescooped by Bernard Ryefield from Papers
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Networks of military alliances, wars, and international trade

The incidence of interstate wars has dropped dramatically over time: The number of wars per pair of countries per year from 1950 to 2000 was roughly a 10th as high as it was from 1820 to 1949. This significant decrease in the frequency of wars correlates with a substantial increase in the number of military alliances per country and the stability of those alliances. We show that one possible explanation of this is an accompanying expansion of international trade. Increased trade decreases countries’ incentives to attack each other and increases their incentives to defend each other, leading to a stable and peaceful network of military and trade alliances that is consistent with observed data.

 

Networks of military alliances, wars, and international trade
Matthew O. Jackson and Stephen Nei

PNAS 112(50):15277–15284

http://dx.doi.org/10.1073/pnas.1520970112 ;


Via Complexity Digest
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ukituki's curator insight, December 19, 2015 4:40 PM
Based on the model we also examine some specific relationships, finding that countries with high levels of trade with their allies are less likely to be involved in wars with any other countries (including allies and nonallies), and that an increase in trade between two countries correlates with a lower chance that they will go to war with each other.
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Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

Eigencentrality based on dissimilarity measures reveals central nodes in complex networks | Networks and Graphs | Scoop.it
One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network.
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Barry Schachter's curator insight, November 27, 2015 9:12 AM

Interesting refinement of centrality measure such that more importance is given to centrality of neighboring nodes with fewer mutual connections. For example, the logic suggests that  a neighbor B of node A is not important to the centrality of A even if B has many connections, when most of the nodes that B shares edges with already share edges with A. 

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Power networks | plus.maths.org

Barabási and Albert investigated what happens if you build a network randomly, but with the probabilities of a new node attaching itself to existing nodes governed by the rich-get-richer rule — and they found that scale-freeness naturally emerges. It's therefore stands to reason that scale-freeness is a result of the rich getting richer, even in networks that are not, strictly speaking, random.

Being scale free can be good and bad at the same time. On the one hand, there are many nodes with relatively few connections. So if bad weather disables a random node in a scale free transport network, then this isn't necessarily such a disaster. Chances are that the node doesn't have too many connections, so its failure isn't going to effect the rest of the network too badly. On the other hand, it's not that highly connected nodes don't exist in a scale free network. If you are a terrorist trying to disrupt the network, you should try to take out one of them, disabling much of the network in an instant.

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Fractal and Small-World Networks Formed by Self-Organized Critical Dynamics

Fractal and Small-World Networks Formed by Self-Organized Critical Dynamics | Networks and Graphs | Scoop.it

We propose a dynamical model in which a network structure evolves in a self-organized critical (SOC) manner and explain a possible origin of the emergence of fractal and small-world networks. Our model combines a network growth and its decay by failures of nodes. The decay mechanism reflects the instability of large functional networks against cascading overload failures. It is demonstrated that the dynamical system surely exhibits SOC characteristics, such as power-law forms of the avalanche size distribution, the cluster size distribution, and the distribution of the time interval between intermittent avalanches. During the network evolution, fractal networks are spontaneously generated when networks experience critical cascades of failures that lead to a percolation transition. In contrast, networks far from criticality have small-world structures. We also observe the crossover behavior from fractal to small-world structure in the network evolution.

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Topology and evolution of the network of western classical music composers

Topology and evolution of the network of western classical music composers | Networks and Graphs | Scoop.it
The expanding availability of high-quality, large-scale data from the realm of culture and the arts promises novel opportunities for understanding and harnessing the dynamics of the creation, collaboration, and dissemination processes - fundamentally network phenomena - of artistic works and styles. To this end, in this paper we explore the complex network of western classical composers constructed from a comprehensive CD (Compact Disc) recordings data that represent the centuries-old musical tradition using modern data analysis and modeling techniques. We start with the fundamental properties of the network such as the degree distribution and various centralities, and find how they correlate with composer attributes such as artistic styles and active periods, indicating their significance in the formation and evolution of the network. We also investigate the growth dynamics of the network, identifying superlinear preferential attachment as a major growth mechanism that implies a future of the musical landscape where an increasing concentration of recordings onto highly-recorded composers coexists with the diversity represented by the growth in the sheer number of recorded composers. Our work shows how the network framework married with data can be utilized to advance our understanding of the underlying principles of complexities in cultural systems.
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Voting Behaviour and Power in Online Democracy: A Study of LiquidFeedback in Germany's Pirate Party

In recent years, political parties have adopted Online Delegative Democracy platforms such as LiquidFeedback to organise themselves and their political agendas via a grassroots approach. A common objection against the use of these platforms is the delegation system, where a user can delegate his vote to another user, giving rise to so-called super-voters, i.e. powerful users who receive many delegations. It has been asserted in the past that the presence of these super-voters undermines the democratic process, and therefore delegative democracy should be avoided. In this paper, we look at the emergence of super-voters in the largest delegative online democracy platform worldwide, operated by Germany's Pirate Party. We investigate the distribution of power within the party systematically, study whether super-voters exist, and explore the influence they have on the outcome of votings conducted online. While we find that the theoretical power of super-voters is indeed high, we also observe that they use their power wisely. Super-voters do not fully act on their power to change the outcome of votes, but they vote in favour of proposals with the majority of voters in many cases thereby exhibiting a stabilising effect on the system. We use these findings to present a novel class of power indices that considers observed voting biases and gives significantly better predictions than state-of-the-art measures.

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Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks

Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks | Networks and Graphs | Scoop.it

Recently, the authors proposed a quantum prisoner’s dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner’s dilemma (GPD, for short) games based on the weak Prisoner’s dilemma game, the full prisoner’s dilemma game and the normalized Prisoner’s dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C), defector (D) and super cooperator (denoted by Q), and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner’s dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence) of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner’s dilemma games.

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EPJ Data Science | Full text | The nature and evolution of online food preferences

Food is a central element of humans’ life, and food preferences are amongst others manifestations of social, cultural and economic forces that influence the way we view, prepare and consume food. Historically, data for studies of food preferences stems from consumer panels which continuously capture food consumption and preference patterns from individuals and households. In this work we look at a new source of data, i.e., server log data from a large recipe platform on the World Wide Web, and explore its usefulness for understanding online food preferences. The main findings of this work are: (i) recipe preferences are partly driven by ingredients, (ii) recipe preference distributions exhibit more regional differences than ingredient preference distributions, and (iii) weekday preferences are clearly distinct from weekend preferences.
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Inuit Genealogy

Inuit Genealogy | Networks and Graphs | Scoop.it
The diagram above is a genealogical diagram made in the mid 1950s by anthropologist Jean Malaurie, the first of its kind. It’s a hand made radial drawing, Malaurie has a whole series of them in his apartment in Paris, along with his extensive personal archive of research materials including photos, films, notebooks, drawings.
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Introduction to focus issue: Patterns of network synchronization

The study of synchronization of coupled systems is currently undergoing a major surge fueled by recent discoveries of new forms of collective dynamics and the development of techniques to characterize a myriad of new patterns of network synchronization. This includes chimera states, phenomena determined by symmetry, remote synchronization, and asymmetry-induced synchronization. This Focus Issue presents a selection of contributions at the forefront of these developments, to which this introduction is intended to offer an up-to-date foundation.

 

Introduction to focus issue: Patterns of network synchronization Daniel M. Abrams, Louis M. Pecora and Adilson E. Motter

Chaos 26, 094601 (2016); http://dx.doi.org/10.1063/1.4962970


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Social Media Data Reveals What Hoods Are Gentrifying

Social Media Data Reveals What Hoods Are Gentrifying | Networks and Graphs | Scoop.it
Censuses give a poor impression of the socioeconomic standing of a neighborhood. Twitter, meanwhile, does a pretty good job.
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Shakespeare tragedies as network graphs

Shakespeare tragedies as network graphs | Networks and Graphs | Scoop.it

Martin Grandjean looked at the structure of Shakespeare tragedies through character interactions. Each circle (node) represents a character, and each connecting line (edge) represents two characters who appeared in the same scene.

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Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks | Networks and Graphs | Scoop.it

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

 (Illustrative videos can be downloaded from here:this http URL)

 

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely

http://arxiv.org/abs/1511.01238 ;


Via Complexity Digest, Bernard Ryefield
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Complexity Digest's curator insight, November 18, 2015 6:13 PM

See Also: http://networkdecisions.linkgroup.hu 

António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

 

http://www.researchgate.net/publication/273527384_Constructal_Law_Optimization_as_Design_Evolution

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How Many Real Friends Can You Have at Once? - Facts So Romantic - Nautilus

How Many Real Friends Can You Have at Once? - Facts So Romantic - Nautilus | Networks and Graphs | Scoop.it
My wife can’t seem to walk for a half-hour around Ottawa, a city with nearly a million people, without running into at least three…
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The New Laws of Explosive Networks | Quanta Magazine

The New Laws of Explosive Networks |  Quanta Magazine | Networks and Graphs | Scoop.it
Researchers are uncovering the hidden laws that reveal how the Internet grows, how viruses spread, and how financial bubbles burst.
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Dynamical Systems on Networks: A Tutorial

Dynamical Systems on Networks: A Tutorial | Networks and Graphs | Scoop.it

We give a tutorial for the study of dynamical systems on networks. We focus especially on "simple" situations that are tractable analytically, because they can be very insightful and provide useful springboards for the study of more complicated scenarios. We briefly motivate why examining dynamical systems on networks is interesting and important, and we then give several fascinating examples and discuss some theoretical results. We also briefly discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give an outlook on the field.

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A 1:1000 scale model of the digital world: Global connectivity can lead to the extinction of local networks

The overwhelming success of online social networks, the key actors in the cosmos of the Web 2.0, has reshaped human interactions on a worldwide scale. To understand the fundamental mechanisms which determine the fate of online social networks at the system level, we recently introduced a general ecological theory of the digital world. In this paper, we discuss the impact of heterogeneity in the network intrinsic fitness and present how the general theory can be applied to understand the competition between an international network, like Facebook, and local services. To this end, we construct a 1:1000 scale model of the digital world enclosing the 80 countries with most Internet users. We find that above a certain threshold the level of global connectivity can lead to the extinction of local networks. In addition, we reveal the complex role the tendency of individuals to engage in more active networks plays for the probability of local networks to become extinct and provide insights into the conditions under which they can prevail.

 

A 1:1000 scale model of the digital world: Global connectivity can lead to the extinction of local networks
Kaj-Kolja Kleineberg, Marian Boguna

http://arxiv.org/abs/1504.01368


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Networks Reveal the Connections of Disease | Quanta Magazine

Networks Reveal the Connections of Disease |  Quanta Magazine | Networks and Graphs | Scoop.it
Enormous databases of medical records have begun to reveal the hidden biological missteps that make us sick.

Via Roger D. Jones, PhD
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Rescooped by Bernard Ryefield from Non-Equilibrium Social Science
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Systematic inequality and hierarchy in faculty hiring networks

The faculty job market plays a fundamental role in shaping research priorities, educational outcomes, and career trajectories among scientists and institutions. However, a quantitative understanding of faculty hiring as a system is lacking. Using a simple technique to extract the institutional prestige ranking that best explains an observed faculty hiring network—who hires whose graduates as faculty—we present and analyze comprehensive placement data on nearly 19,000 regular faculty in three disparate disciplines. Across disciplines, we find that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality. Furthermore, doctoral prestige alone better predicts ultimate placement than a U.S. News & World Report rank, women generally place worse than men, and increased institutional prestige leads to increased faculty production, better faculty placement, and a more influential position within the discipline. These results advance our ability to quantify the influence of prestige in academia and shed new light on the academic system.

 

Systematic inequality and hierarchy in faculty hiring networks
Aaron Clauset, Samuel Arbesman, Daniel B. Larremore

Science Advances 01 Feb 2015: Vol. 1 no. 1 e1400005

http://dx.doi.org/10.1126/sciadv.1400005 ;


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Représenter les généalogies intellectuelles : des Successions à Wikidata | Sciences communes

Représenter les généalogies intellectuelles : des Successions à Wikidata | Sciences communes | Networks and Graphs | Scoop.it

C'est une obsession ancienne. Dès le IIIe siècle avant notre ère, des Successions de philosophes dressent la généalogie des relations entre maîtres et élèves.

L'obsession est encore bien vivante dans certaines disciplines. Mathématiciens, astronomes et chimistes tiennent ainsi à jour des bases de données de généalogie universitaire ; ils en tirent parfois des indices de proximité (ainsi, l'indice d'Erdös, qui désigne le degré de distance de tel mathématicien avec Paul Erdös). Ces initiatives restent partielles : elles sont cantonnées à une seule culture disciplinaire.

Le projet Wikidata permet d'aller au-delà : il aspire à composer une base de connaissance universelle. Sa communauté améliore et reformule continuellement une ontologie couvrant la totalité du savoir humain. Elle a ainsi créé depuis quelques mois une catégorie "étudiant de" (alias P1066), qui permet de signaler que X a étudié avec Y.

C'est ainsi que l'obsession m'a pris.

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