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Rescooped by Bernard Ryefield from Complexity & Systems
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Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions of every player over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in western countries. In particular we find an approximate exponential for low wealth and a power-law tail. The Gini index is found to be 0.65, which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degree in the trade network, relatively low nearest-neighbor degree and a low clustering coefficient. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. We find that players that are not organized within social groups with at least three members are significantly poorer on average. We observe that high `political' status and high wealth go hand in hand. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies.

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Eli Levine's curator insight, April 5, 2014 10:53 AM

When you let laissez-faire take its course, only a few individuals really end up on top.  That's not to say that markets shouldn't be allowed and enabled to exist, for the sake of the free exchange of goods, services, knowledge, wealth, etc.  It is saying that we need non-intrusive mechanisms to help make sure that the wealth that is produced is enjoyed by everyone who produced it.

 

Some people will always have more than others, for behavioral reasons and for circumstantial reasons.  That is not a problem, in my own view.  The problem comes, for me, when their focus on wealth becomes so great that they lose sight of their human needs on the individual as well as social and environmental levels, such that they choose wealth that they will not use over that which they need for survival and physical/psychological well being.

 

It's a form of being disconnected with the real world, kind of like schizophrenia.  The brain isn't functioning properly when  greed is and has taken over, for one reason or another.  It should be considered a mental illness that we could, potentially in time, treat, such that these individuals who are not aware and do not care to be aware of their actual place in the universe can lead normal, happy, healthy and appropriately placed lives in our societies.

 

So, we're left with the present situation in which work is undervalued, relative to what it produces, while executive management is way overvalued relative to its healthy role in the economy and society.  I'm not saying that pure equality is desirable, because sometimes people do work harder than others and deserve a greater share of wealth than someone who didn't work when they honestly could have.  What I'm saying, is that indulging the elite's fantasy of the ego is detrimental to themselves and to others, and that I don't think it should be accepted or tolerated within our social world.

 

If you want equality of opportunities, you must have more equality of outcomes.  That is yet another fact about our world that conservatives fail to accept and appreciate, if they're attempting to realize a world in which we are all together as one, rather than a world where we are heavily stratified according to an artificial hierarchy.  That is the difference between a conservative and a progressive.  One wants us all to be living together in peace, harmony, stability and, for want of a better word, love, while the other just wants everyone in a specific place according to birth.  One promotes democracy and inclusivity, the other, discourages it.  One works better for humanity on the tangible level, the other, does not.

 

And it's just a difference in brain type/values that makes them be something so antithetical to what Western civilization has stood for.

 

Think about it.

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Dynamical Systems on Networks: A Tutorial

We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give our outlook on the field.

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Using Complex Networks to Characterize International Business Cycles

Using Complex Networks to Characterize International Business Cycles | Networks and Graphs | Scoop.it
Background

There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.

Methodology/Principal Findings

We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.

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Eli Levine's curator insight, March 29, 2014 3:13 PM

These are the natural laws and connections which exist amongst various economies and within each economy.  This shows the interconnectedness of the whole planet's economy and can give predictions as to what could happen if one particular economy were to crash and fall into valuelessness for humanity.

 

It's interesting that this research comes at a time in our history when the natural laws of social interactions are being violated by governments and elite groups everywhere.  What will happen if discontent turns into unrest and rebellions in the United States?  What happens if the authority of governments ceases to be legitimate, to the point where violence and anarchy take their place.  What will happen to the economy if the rule of law is no longer abided, and the mob takes over to deal with the perceived injustices that the elite groups have committed against the general public?

 

What happens when the environment gives way and our societies are no longer able to support the populations that are present?  What happens when people are forced to either starve or fight?

 

That's the direction that we're headed towards, I'm afraid. 

Funny how it is that the conservatives from all parties who enacted these policies, are leading to the very destruction of society that they're so afraid of.  Funny how it is that things get more delicate and likely to change significantly as they cling to their image of how the past was (and it is just an image of the past, not the real world as it was, is or will be).

 

Silly brains.

 

Think about it.

António F Fonseca's curator insight, March 31, 2014 6:34 AM

Crisis transmission, lookout for USA, Ireland and Spain!

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From bridges to networks | plus.maths.org

From bridges to networks | plus.maths.org | Networks and Graphs | Scoop.it

One of our favourite maths problems is called the bridges of Königsberg. It involves finding a path on an 18th century map of the city of Königsberg that crosses each of its seven bridges once and only once — or proving that there isn't one. We love the problem because its solution, provided by Leonhard Euler in 1735, is elegant and simple, just what a good solution should be (see here). But that's not all there is to it. Euler's solution also laid the foundation for an area of maths that couldn't be more relevant to modern life: network theory. Here's why.

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So We Have No Free Will, But At Least We Are Influencing Half Of The World

So We Have No Free Will, But At Least We Are Influencing Half Of The World | Networks and Graphs | Scoop.it

In times of easy access to the Internet and cheap travel, we consider ourselves part of a global society, but how connected this really makes us will surprise many of us.

A Portuguese research group has found that social networks are allowing us to influence people everywhere, and not only those that we know, but also people that we never or will ever meet, which is nothing short of extraordinary.

 

 

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KONECT - The Koblenz Network Collection

KONECT - The Koblenz Network Collection | Networks and Graphs | Scoop.it

KONECT is the Koblenz Network Collection. KONECT is a project to collect large network datasets of all types in order to perform research in the area of network mining, collected by the Institute of Web Science and Technologies of the University of Koblenz–Landau. KONECT contains over a hundred network datasets of various types.

A network as provided by KONECT is a set of nodes connected by links. An example of a network is a social network: a set of users connected by links which represent friendship relations. A network is represented mathematically by a graph, in which nodes are called vertices and links are called edges.

Most networks are asymmetric: The fact that user A follows user B on the microblogging site Twitter does not imply that user B follows user A. The Twitter graph is thus directed. In the DBLP authorship network, scientific publications are connected to their authors. The DBLP publication network thus has two classes of nodes; it is bipartite.

KONECT provides:

Code to generate all network datasets from the Web
Statistics and plots viewable online
Download of selected datasets (where legally possible)

To be added in the future:

Analysis code to generate all statistics and plots

 

 

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Amazing Video Clips Visually Isolate the Flight Paths of Birds | Colossal

Amazing Video Clips Visually Isolate the Flight Paths of Birds | Colossal | Networks and Graphs | Scoop.it

Chances are if you’ve on the internet over the last few years you’ve run into a few amazing bird murmuration videos, like this one from Islands and Rivers or the one we featured on Colossal from Neels Castillion, where countless numbers of starlings flock together and move almost impossibly in concert. Artist Dennis Hlynsky, a professor at the Rhode Island School of Design, wondered what would happen if he could better trace the flight paths of individual birds, what kinds of patterns would emerge from these flying social networks?

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Connecting Dream Networks Across Cultures

Connecting Dream Networks Across Cultures | Networks and Graphs | Scoop.it

Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world. We analyze communities in these networks, finding that symbols within a community are semantically related. The central nodes in communities give insight about cultures and symbols in dreams. The community structure of different networks highlights cultural similarities and differences. Interconnections between different networks are also identified by translating symbols from different languages into English. Structural correlations across networks point out relationships between cultures.Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative,and neutral dream symbols.

 

 

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Rescooped by Bernard Ryefield from Social Network Analysis #sna
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Network Science Book by Albert Laszlo Barabasi

Network Science Book by Albert Laszlo Barabasi | Networks and Graphs | Scoop.it
The power of network science, the beauty of network visualization.

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Classifying Latent Infection States in Complex Networks

Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing infections. Often, however, only a relatively small set of nodes have a known infection state. Moreover, the length of time that each node has been infected is also unknown. This missing data -- infection state of most nodes and infection time of the unobserved infected nodes -- poses a challenge to the study of real-world cascades.
In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on the likely epidemic paths predicted by the simple susceptible-infected epidemic model, we propose a measure (Infection Betweenness) for uncovering these unknown infection states. Our experimental results using machine learning algorithms show that Infection Betweenness is the most effective feature for identifying latent infected nodes.

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Rescooped by Bernard Ryefield from Papers
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Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries

Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries | Networks and Graphs | Scoop.it

Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.

 

Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, et al. (2013) Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries. PLoS ONE 8(12): e81707. http://dx.doi.org/10.1371/journal.pone.0081707


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Supporting MOOC Instruction with Social Network Analysis

Supporting MOOC Instruction with Social Network Analysis | Networks and Graphs | Scoop.it

With an expansive and ubiquitously available gold mine of educational data,Massive Open Online courses (MOOCs) have become an important foci of learning analytics research. In this paper, we investigate potential reasons as to why are these digitalized learning repositories being plagued with huge attrition rates. We analyze an ongoing online course offered in Coursera using a social network perspective, with an objective to identify students who are actively participating in course discussions and those who are potentially at a risk of dropping off. We additionally perform extensive forum analysis to visualize student's posting patterns longitudinally. Our results provide insights that can assist educational designers in establishing a pedagogical basis for decision-making while designing MOOCs. We infer prominent characteristics about the participation patterns of distinct groups of studentsin the networked learning community, and effectively discover important discussion threads. These methods can, despite the otherwise prohibitive numberof students involved, allow an instructor to leverage forum behavior to identify opportunities for support.

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Information Evolution in Social Networks

Social networks readily transmit information, albeit with less than perfect delity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and
evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities.
A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the di usion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer \laterally" between di erent memes. Subpopulations of the social network can preferentially transmit a speci c variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in di using information has important implications for how we interpret and harness the information that reaches us through our social networks.

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Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media

Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media | Networks and Graphs | Scoop.it

The subjective nature of gender inequality motivates the analysis and comparison of data from real and fictional human interaction. We present a computational extension of the Bechdel test: A popular tool to assess if a movie contains a male gender bias, by looking for two female characters who discuss about something besides a man. We provide the tools to quantify Bechdel scores for both genders, and we measure them in movie scripts and large datasets of dialogues between users of MySpace and Twitter. Comparing movies and users of social media, we find that movies and Twitter conversations have a consistent male bias, which does not appear when analyzing MySpace.Furthermore, the narrative of Twitter is closer to the movies that do not pass the Bechdel test than to those that pass it.
We link the properties of movies and the users that share trailers of those movies. Our analysis reveals some particularities of movies that pass the Bechdel test: Their trailers are less popular, female users are more likely to share them than male users, and users that share them tend to interact less with male users. Based on our datasets, we define gender independence measurements to analyze the gender biases of a society, as manifested through digital traces of online behavior. Using the profile information of Twitter users, we find larger gender independence for urban users in comparison to rural ones. Additionally, the asymmetry between genders is larger for parents and lower for students. Gender asymmetry varies across US states, increasing with higher average income and latitude. This points to the relation between gender inequality and social, economical, and cultural factors of a society,and how gender roles exist in both fictional narratives and public online dialogues.

 

 

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Generalized friendship paradox in complex networks: The case of scientific collaboration

The friendship paradox states that your friends have on average more friends than you have. Does the paradox "hold" for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.
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Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory | Networks and Graphs | Scoop.it

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

 

 

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Thoughts on SNA and online learning

Thoughts on SNA and online learning | Networks and Graphs | Scoop.it
Following the previous post... The structural paradigm of  Social Network Analysis (SNA) with its constitutive theory and methods, began to emerge around the 1930s, applied and influenced by a broa...

Via Dr. Susan Bainbridge, Marinella De Simone
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Milena Bobeva's curator insight, March 1, 2014 4:10 AM

Social Network Analysis should be a  paradigm for researching, designing, and evaluating not only online learning, but  the wider phenomenon of Education 3.0

luiy's curator insight, March 1, 2014 7:21 PM

The connections within nodes in a network facilitate exchange of “resources”  which can be influenced by the quantity and quality of the linkages and interactions. Looking at online educational networks through a SNA lens is a way to establish wether the ways in which individuals connect with a particular environment may influence their access to information and knowledge. As Rita Kop states “the Web is portrayed as a democratic network on which peer to peer interaction might lead to a creative explosion and participative culture of activity” (Kop, 2012 p3) but how is this potential being exploited in education? What are the processes beyond this interaction and how can they be used to facilitate students access to information, knowledge and ideas?

 

The potential of social media in forming networks, extending students knowledge and translating this into academic achievement is impacted by a multitude of elements such as individuals’ attitudes (Morrison, 2002), University environment and socialisation processes (Yu et al., 2010). Other mechanisms influencing this process may be the particular educational practices and experiences, the success of connections, the dynamics in which participants negotiate the structure of the network and exchange practices and many others which can not be controlled.

 

This analysis can be enriched by Bordieau’s concept of “social capital”, which introduces a set of dynamics between the social dimension, the identity dimension (habitus) and the individual’s practice. In this system of reciprocal influences it is interesting to look at the transformation processes and effects of elements such as “weak ties”, “brokers”, “latent connections” and “structural holes” in the information flow within a network.

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21 Technologies That Will Decentralize the World

21 Technologies That Will Decentralize the World | Networks and Graphs | Scoop.it
Across the planet, new technologies and business models are decentralizing power and placing it in the hands of communities and individuals. 
"We are seeing technology-driven networks replacing bureacratically-driven hierarchies," says VC and futurist Fred Wilson, speaking on what to expect in the next ten years. View the entire 25-minute video below (it's worth it!) and then check out the 21 innovations below.

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june holley's curator insight, February 26, 2014 12:26 PM

These will help networks develop in new ways.

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Ambiguity in language networks

Ambiguity in language networks | Networks and Graphs | Scoop.it

Human language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element of language architecture. Here we review the evidence both from language network architecture and from theoretical reasonings based on a least effort argument.Ambiguity is shown to play an essential role in providing a source of language efficiency, and is likely to be an inevitable byproduct of network growth.

 

 

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A Network Characteristic That Correlates Environmental and Genetic Robustness

A Network Characteristic That Correlates Environmental and Genetic Robustness | Networks and Graphs | Scoop.it

As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

 

 

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Frontiers | Walking across Wikipedia: a scale-free network model of semantic memory retrieval | Frontiers in Language Sciences

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like “animals” are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.
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Top 11 Free Software for Text Analysis, Text Mining, Text Analytics - Predictive Analytics Today

Top 11 Free Software for Text Analysis, Text Mining, Text Analytics - Predictive Analytics Today | Networks and Graphs | Scoop.it
Review of Top 11 Free Software for Text Analysis, Text Mining, Text Analytics ? KH Coder, Carrot2, GATE, tm, Gensim, Natural Language Toolkit, RapidMiner, Unstructured Information Management Architecture, OpenNLP, KNIME, Orange-Textable and LPU are some of the key vendors who provides text analytics software

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Double percolation phase transition in clustered complex networks

We perform an extensive numerical study of the effects of clustering on the structural properties of complex networks. We observe that strong clustering in heterogeneous networks induces the emergence of a core-periphery organization that has a critical effect on their percolation properties. In such situation, we observe a novel double phase transition, with an intermediate phase where only the core of the network is percolated, and a final phase where the periphery percolates regardless of the core. Interestingly, strong clustering makes simultaneously the core more robust and the periphery more fragile. These phenomena are also found in real complex networks.

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Le Knowledge Graph de Google ferait baisser le trafic de Wikipedia - Actualité Abondance

Le Knowledge Graph de Google ferait baisser le trafic de Wikipedia - Actualité Abondance | Networks and Graphs | Scoop.it
14 janvier 2014 - La fondation Wikipedia a publié ses statistiques de trafic pour l'année 2013, en baisse dans de nombreux pays. Une hypothèse évoquée serait la croissance d par Actualité Abondance

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Epidemiological modeling of online social network dynamics

The last decade has seen the rise of immense online social networks (OSNs) such as MySpace and Facebook. In this paper we use epidemiological models to explain user adoption and abandonment of OSNs, where adoption is analogous to infection and abandonment is analogous to recovery. We modify the traditional SIR model of disease spread by incorporating infectious recovery dynamics such that contact between a recovered and infected member of the population is required for recovery. The proposed infectious recovery SIR model (irSIR model) is validated using publicly available Google search query data for "MySpace" as a case study of an OSN that has exhibited both adoption and abandonment phases. The irSIR model is then applied to search query data for "Facebook," which is just beginning to show the onset of an abandonment phase. Extrapolating the best fit model into the future predicts a rapid decline in Facebook activity in the next few years.

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