Network and Graph Theory | Scoop.it
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All the new curated posts for the topic: Network and Graph TheorySun, 24 May 2015 09:32:24 GMTBernard Ryefield2015-05-24T09:32:24ZNetwork and Graph Theory | Scoop.ithttp://img.scoop.it/2aV7IY53q0JSXHMEqDDrw396MkXN2Bo-CBIMTdOCamM=
http://www.scoop.it/t/network-and-graph-theory
Network Science by Albert-László Barabási
http://www.scoop.it/t/network-and-graph-theory/p/4043789932/2015/05/18/network-science-by-albert-laszlo-barabasi
<img src='http://img.scoop.it/2aV7IY53q0JSXHMEqDDrwzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>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.</p><p>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.</p><img src='http://www.scoop.it/rv?p=4043789932&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4043789932/2015/05/18/network-science-by-albert-laszlo-barabasi'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Dynamical Systems on Networks: A Tutorial
http://www.scoop.it/t/network-and-graph-theory/p/4043256494/2015/05/10/dynamical-systems-on-networks-a-tutorial
<img src='http://img.scoop.it/W30JQtcsVoutDAog4sQ0xzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>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.</p><img src='http://www.scoop.it/rv?p=4043256494&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4043256494/2015/05/10/dynamical-systems-on-networks-a-tutorial'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Topology and evolution of the network of western classical music composers
http://www.scoop.it/t/network-and-graph-theory/p/4042146029/2015/04/24/topology-and-evolution-of-the-network-of-western-classical-music-composers
<img src='http://img.scoop.it/l8LTX73gZpjRv3xOhZHtBzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> 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.</blockquote><img src='http://www.scoop.it/rv?p=4042146029&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4042146029/2015/04/24/topology-and-evolution-of-the-network-of-western-classical-music-composers'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>A 1:1000 scale model of the digital world: Global connectivity can lead to the extinction of local networks
http://www.scoop.it/t/network-and-graph-theory/p/4041029236/2015/04/09/a-1-1000-scale-model-of-the-digital-world-global-connectivity-can-lead-to-the-extinction-of-local-networks
<p>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.</p><p> </p><p>A 1:1000 scale model of the digital world: Global connectivity can lead to the extinction of local networks<br>Kaj-Kolja Kleineberg, Marian Boguna</p><p><a href="http://arxiv.org/abs/1504.01368" rel="nofollow" target="_blank">http://arxiv.org/abs/1504.01368</a></p><img src='http://www.scoop.it/rv?p=4041029236&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4041029236/2015/04/09/a-1-1000-scale-model-of-the-digital-world-global-connectivity-can-lead-to-the-extinction-of-local-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Voting Behaviour and Power in Online Democracy: A Study of LiquidFeedback in Germany's Pirate Party
http://www.scoop.it/t/network-and-graph-theory/p/4040097638/2015/03/27/voting-behaviour-and-power-in-online-democracy-a-study-of-liquidfeedback-in-germany-s-pirate-party
<p>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.</p><img src='http://www.scoop.it/rv?p=4040097638&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4040097638/2015/03/27/voting-behaviour-and-power-in-online-democracy-a-study-of-liquidfeedback-in-germany-s-pirate-party'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Networks Reveal the Connections of Disease | Quanta Magazine
http://www.scoop.it/t/network-and-graph-theory/p/4037495116/2015/02/18/networks-reveal-the-connections-of-disease-quanta-magazine
<img src='http://img.scoop.it/MFqVwgpE4Zp116roo-Z5wjl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> Enormous databases of medical records have begun to reveal the hidden biological missteps that make us sick.</blockquote><img src='http://www.scoop.it/rv?p=4037495116&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4037495116/2015/02/18/networks-reveal-the-connections-of-disease-quanta-magazine'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks
http://www.scoop.it/t/network-and-graph-theory/p/4037277714/2015/02/14/emergence-of-super-cooperation-of-prisoner-s-dilemma-games-on-scale-free-networks
<img src='http://img.scoop.it/uZEKQLxi2r7PNtN5EVjfdDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>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.</p><img src='http://www.scoop.it/rv?p=4037277714&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4037277714/2015/02/14/emergence-of-super-cooperation-of-prisoner-s-dilemma-games-on-scale-free-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Systematic inequality and hierarchy in faculty hiring networks
http://www.scoop.it/t/network-and-graph-theory/p/4037234831/2015/02/13/systematic-inequality-and-hierarchy-in-faculty-hiring-networks
<p>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.</p><p> </p><p>Systematic inequality and hierarchy in faculty hiring networks<br>Aaron Clauset, Samuel Arbesman, Daniel B. Larremore</p><p>Science Advances 01 Feb 2015: Vol. 1 no. 1 e1400005</p><p><a href="http://dx.doi.org/10.1126/sciadv.1400005 " rel="nofollow" target="_blank">http://dx.doi.org/10.1126/sciadv.1400005 </a>;</p><img src='http://www.scoop.it/rv?p=4037234831&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4037234831/2015/02/13/systematic-inequality-and-hierarchy-in-faculty-hiring-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>EPJ Data Science | Full text | The nature and evolution of online food preferences
http://www.scoop.it/t/network-and-graph-theory/p/4035020223/2015/01/10/epj-data-science-full-text-the-nature-and-evolution-of-online-food-preferences
<blockquote> 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.</blockquote><img src='http://www.scoop.it/rv?p=4035020223&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4035020223/2015/01/10/epj-data-science-full-text-the-nature-and-evolution-of-online-food-preferences'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Représenter les généalogies intellectuelles : des Successions à Wikidata | Sciences communes
http://www.scoop.it/t/network-and-graph-theory/p/4033758529/2014/12/17/representer-les-genealogies-intellectuelles-des-successions-a-wikidata-sciences-communes
<img src='http://img.scoop.it/Jz7XHw-AsfPqtKdcwQ3FATl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>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.</p><p>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.</p><p>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.</p><p>C'est ainsi que l'obsession m'a pris.</p><img src='http://www.scoop.it/rv?p=4033758529&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4033758529/2014/12/17/representer-les-genealogies-intellectuelles-des-successions-a-wikidata-sciences-communes'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Inuit Genealogy
http://www.scoop.it/t/network-and-graph-theory/p/4030952909/2014/11/02/inuit-genealogy
<img src='http://img.scoop.it/s_UXhPfAJtHR69FxOtw-Xzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> 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.</blockquote><img src='http://www.scoop.it/rv?p=4030952909&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4030952909/2014/11/02/inuit-genealogy'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>The small-world effect is a modern phenomenon
http://www.scoop.it/t/network-and-graph-theory/p/4027062873/2014/08/28/the-small-world-effect-is-a-modern-phenomenon
<img src='http://img.scoop.it/zdIP3XEjqPaX3z26Rt4zBDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>The "small-world effect" is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind's tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.</p><img src='http://www.scoop.it/rv?p=4027062873&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4027062873/2014/08/28/the-small-world-effect-is-a-modern-phenomenon'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Global Civil Unrest: Contagion, Self-Organization, and Prediction
http://www.scoop.it/t/network-and-graph-theory/p/4026537033/2014/08/19/global-civil-unrest-contagion-self-organization-and-prediction
<img src='http://img.scoop.it/Cu7Ii3znpDKFyz4hmqvc9Dl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.</p><p> </p><p> </p><img src='http://www.scoop.it/rv?p=4026537033&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4026537033/2014/08/19/global-civil-unrest-contagion-self-organization-and-prediction'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Network Effects on Scientific Collaborations
http://www.scoop.it/t/network-and-graph-theory/p/4026534729/2014/08/19/network-effects-on-scientific-collaborations
<img src='http://img.scoop.it/eEEU_4WXIccqQMiFngaDnzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of ‘steel structure’ for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.</p><img src='http://www.scoop.it/rv?p=4026534729&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4026534729/2014/08/19/network-effects-on-scientific-collaborations'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Multilayer networks
http://www.scoop.it/t/network-and-graph-theory/p/4025867788/2014/08/06/multilayer-networks
<img src='http://img.scoop.it/NSWBNQwtgECybM3-IY-tZzl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize ‘traditional’ network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other and provide a thorough discussion that compares, contrasts and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.</p><img src='http://www.scoop.it/rv?p=4025867788&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4025867788/2014/08/06/multilayer-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>The Social Network of Alexander the Great: Social Network Analysis in Ancient History
http://www.scoop.it/t/network-and-graph-theory/p/4025389505/2014/07/28/the-social-network-of-alexander-the-great-social-network-analysis-in-ancient-history
<img src='http://img.scoop.it/qED9rSxzkQnqwq0A7WClHDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> The Social Network of Alexander the Great: Social Network Analysis in Ancient History</blockquote><img src='http://www.scoop.it/rv?p=4025389505&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4025389505/2014/07/28/the-social-network-of-alexander-the-great-social-network-analysis-in-ancient-history'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Online Social Networks: Threats and Solutions
http://www.scoop.it/t/network-and-graph-theory/p/4025247678/2014/07/25/online-social-networks-threats-and-solutions
<p>Many online social network (OSN) users are unaware of the numerous security risks that exist in these networks, including privacy violations, identity theft, and sexual harassment, just to name a few. According to recent studies, OSN users readily expose personal and private details about themselves, such as relationship status, date of birth, school name, email address, phone number, and even home address. This information, if put into the wrong hands, can be used to harm users both in the virtual world and in the real world. These risks become even more severe when the users are children. In this paper we present a thorough review of the different security and privacy risks which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users. We also offer simple-to-implement recommendations for OSN users which can improve their security and privacy when using these platforms. Furthermore, we suggest future research directions.</p><img src='http://www.scoop.it/rv?p=4025247678&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4025247678/2014/07/25/online-social-networks-threats-and-solutions'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Information, Meaning, and Intellectual Organization in Networks of Inter-Human Communication
http://www.scoop.it/t/network-and-graph-theory/p/4023504017/2014/06/24/information-meaning-and-intellectual-organization-in-networks-of-inter-human-communication
<p>The Shannon-Weaver model of linear information transmission is extended with two loops potentially generating redundancies: (i) meaning is provided locally to the information from the perspective of hindsight, and (ii) meanings can be codified differently and then refer to other horizons of meaning. Thus, three layers are distinguished: variations in the communications, historical organization at each moment of time, and evolutionary self-organization of the codes of communication over time. Furthermore, the codes of communication can functionally be different and then the system is both horizontally and vertically differentiated. All these subdynamics operate in parallel and necessarily generate uncertainty. However, meaningful information can be considered as the specific selection of a signal from the noise; the codes of communication are social constructs that can generate redundancy by giving different meanings to the same information. Reflexively, one can translate among codes in more elaborate discourses. The second (instantiating) layer can be operationalized in terms of semantic maps using the vector space model; the third in terms of mutual redundancy among the latent dimensions of the vector space. Using Blaise Cronin's {\oe}uvre, the different operations of the three layers are demonstrated empirically.</p><img src='http://www.scoop.it/rv?p=4023504017&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4023504017/2014/06/24/information-meaning-and-intellectual-organization-in-networks-of-inter-human-communication'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>The Strange Evolution of Self Obsession on Reddit — The Physics arXiv Blog — Medium
http://www.scoop.it/t/network-and-graph-theory/p/4023498836/2014/06/24/the-strange-evolution-of-self-obsession-on-reddit-the-physics-arxiv-blog-medium
<img src='http://img.scoop.it/qP9unr0577JGHKZMyGQa8Tl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> The self-proclaimed frontpage of the internet has grown exponentially in just a few years.</blockquote><img src='http://www.scoop.it/rv?p=4023498836&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4023498836/2014/06/24/the-strange-evolution-of-self-obsession-on-reddit-the-physics-arxiv-blog-medium'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Rising Tides or Rising Stars?: Dynamics of Shared Attention on Twitter during Media Events
http://www.scoop.it/t/network-and-graph-theory/p/4023015238/2014/06/16/rising-tides-or-rising-stars-dynamics-of-shared-attention-on-twitter-during-media-events
<img src='http://img.scoop.it/uKhpM5pE0iIXJWM7HOBzRjl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.</blockquote><img src='http://www.scoop.it/rv?p=4023015238&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4023015238/2014/06/16/rising-tides-or-rising-stars-dynamics-of-shared-attention-on-twitter-during-media-events'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Human Genome Variation and the Concept of Genotype Networks
http://www.scoop.it/t/network-and-graph-theory/p/4022975573/2014/06/14/human-genome-variation-and-the-concept-of-genotype-networks
<img src='http://img.scoop.it/cF03yiUSkm9noYXvttkN7zl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.</blockquote><img src='http://www.scoop.it/rv?p=4022975573&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4022975573/2014/06/14/human-genome-variation-and-the-concept-of-genotype-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>▶ Large-Scale Structure in Networks - YouTube
http://www.scoop.it/t/network-and-graph-theory/p/4022602002/2014/06/06/large-scale-structure-in-networks-youtube
<img src='http://img.scoop.it/L8BwltNJ-M86N9rEQtNEKjl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> Mark Newman May 2, 2014 Annual Science Board Symposium and Meeting Complexity: Theory and Practice</blockquote><img src='http://www.scoop.it/rv?p=4022602002&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4022602002/2014/06/06/large-scale-structure-in-networks-youtube'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>ECCS 2014 Living Satellite
http://www.scoop.it/t/network-and-graph-theory/p/4022454009/2014/06/04/eccs-2014-living-satellite
<img src='http://img.scoop.it/-MV_uXhMt9pKXk24y49H7Dl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>Workshop on Robustness, Adaptability and Critical Transitions in Living Systems.Call for papers <a href="http://seis.bristol.ac.uk" rel="nofollow">http://seis.bristol.ac.uk</a>/~fs13378/eccs_2014_livingsys.html</p><p> </p><img src='http://www.scoop.it/rv?p=4022454009&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4022454009/2014/06/04/eccs-2014-living-satellite'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Predicting Successful Memes using Network and Community Structure v2
http://www.scoop.it/t/network-and-graph-theory/p/4022364315/2014/06/02/predicting-successful-memes-using-network-and-community-structure-v2
<p>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.</p><img src='http://www.scoop.it/rv?p=4022364315&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4022364315/2014/06/02/predicting-successful-memes-using-network-and-community-structure-v2'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Influence Spread in Social Networks: A Study via a Fluid Limit of the Linear Threshold Model
http://www.scoop.it/t/network-and-graph-theory/p/4022174568/2014/05/29/influence-spread-in-social-networks-a-study-via-a-fluid-limit-of-the-linear-threshold-model
<p>Threshold based models have been widely used in characterizing collective behavior on social networks. An individual's threshold indicates the minimum level of influence that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. In this work, we begin with a homogeneous version of the Linear Threshold model proposed by Kempe et al. in the context of viral marketing, and generalize this model to arbitrary threshold distributions. We show that the evolution can be modeled as a discrete time Markov chain, and, by using a certain scaling, we obtain a fluid limit that provides an ordinary differential equation model (o.d.e.). We find that the threshold distribution appears in the o.d.e. via its hazard rate function. We demonstrate the accuracy of the o.d.e. approximation and derive explicit expressions for the trajectory of influence under the uniform threshold distribution. Also, for an exponentially distributed threshold, we show that the fluid dynamics are equivalent to the well-known SIR model in epidemiology. We also numerically study how other hazard functions (obtained from the Weibull and loglogistic distributions) provide qualitative different characteristics of the influence evolution, compared to traditional epidemic models, even in a homogeneous setting. We finally show how the model can be extended to a setting with multiple communities and conclude with possible future directions.</p><img src='http://www.scoop.it/rv?p=4022174568&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4022174568/2014/05/29/influence-spread-in-social-networks-a-study-via-a-fluid-limit-of-the-linear-threshold-model'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Shock waves on complex networks
http://www.scoop.it/t/network-and-graph-theory/p/4021798171/2014/05/22/shock-waves-on-complex-networks
<img src='http://img.scoop.it/gO-Kd-8NvfbhBfQgrmWZujl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.</blockquote><img src='http://www.scoop.it/rv?p=4021798171&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4021798171/2014/05/22/shock-waves-on-complex-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>AnaVANET: an experiment and visualization tool for vehicular networks
http://www.scoop.it/t/network-and-graph-theory/p/4021072259/2014/05/10/anavanet-an-experiment-and-visualization-tool-for-vehicular-networks
<img src='http://img.scoop.it/swn5rh5Eq-zC9m-bzJKfrjl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><blockquote> The experimental evaluation of wireless and mobile networks is a challenge that rarely substitutes simulation in research works.</blockquote><img src='http://www.scoop.it/rv?p=4021072259&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4021072259/2014/05/10/anavanet-an-experiment-and-visualization-tool-for-vehicular-networks'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Modeling dynamics of attention in social media with user efficiency
http://www.scoop.it/t/network-and-graph-theory/p/4021067824/2014/05/10/modeling-dynamics-of-attention-in-social-media-with-user-efficiency
<p>Evolution of online social networks is driven by the need of their members to share and consume content, resulting in a complex interplay between individual activity and attention received from others. In a context of increasing information overload and limited resources, discovering which are the most successful behavioral patterns to attract attention is very important. To shed light on the matter, we look into the patterns of activity and popularity of users in the Yahoo Meme microblogging service. We observe that a combination of different type of social and content-producing activity is necessary to attract attention and the efficiency of users, namely the average attention received per piece of content published, for many users has a defined trend in its temporal footprint. The analysis of the user time series of efficiency shows different classes of users whose different activity patterns give insights on the type of behavior that pays off best in terms of attention gathering. In particular, sharing content with high spreading potential and then supporting the attention raised by it with social activity emerges as a frequent pattern for users gaining efficiency over time.</p><p> </p><p>Modeling dynamics of attention in social media with user efficiency<br>Carmen Vaca Ruiz, Luca Maria Aiello and Alejandro Jaimes</p><p>EPJ Data Science 2014, 3:5 <a href="http://dx.doi.org/10.1140/epjds30" rel="nofollow">http://dx.doi.org/10.1140/epjds30</a></p><img src='http://www.scoop.it/rv?p=4021067824&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4021067824/2014/05/10/modeling-dynamics-of-attention-in-social-media-with-user-efficiency'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>How Community Feedback Shapes User Behavior
http://www.scoop.it/t/network-and-graph-theory/p/4020957580/2014/05/08/how-community-feedback-shapes-user-behavior
<p>Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split.</p><img src='http://www.scoop.it/rv?p=4020957580&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4020957580/2014/05/08/how-community-feedback-shapes-user-behavior'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>Measuring Large-Scale Social Networks with High Resolution
http://www.scoop.it/t/network-and-graph-theory/p/4020953482/2014/05/08/measuring-large-scale-social-networks-with-high-resolution
<img src='http://img.scoop.it/DaJr-SPdP78OC1PNPD6apDl72eJkfbmt4t8yenImKBV9ip2J1EIeUzA9paTSgKmv' /><br/><p>This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.</p><p> </p><p>Measuring Large-Scale Social Networks with High Resolution</p><p>Stopczynski A, Sekara V, Sapiezynski P, et al.</p><p>PLoS ONE 9(4): e95978 (2014)</p><p><a href="http://dx.doi.org/10.1371/journal.pone.0095978" rel="nofollow">http://dx.doi.org/10.1371/journal.pone.0095978</a></p><img src='http://www.scoop.it/rv?p=4020953482&tp=Topic'/><br /><br /><div ><a href='http://www.scoop.it/t/network-and-graph-theory/p/4020953482/2014/05/08/measuring-large-scale-social-networks-with-high-resolution'>See it on Scoop.it</a>, via <a href='http://www.scoop.it/t/network-and-graph-theory'>Network and Graph Theory</a></div><div style='clear: both'></div>