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Networks are everywhere... but we are still unaware of their presence and importance
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Origin and Structure of Dynamic Cooperative Networks

Origin and Structure of Dynamic Cooperative Networks | Networks and Big Data | Scoop.it
Societies are built on social interactions among individuals. Cooperation represents the simplest form of a social interaction: one individual provides a benefit to another one at a cost to itself. Social networks represent a dynamical abstraction of social interactions in a society. The behaviour of an individual towards others and of others towards the individual shape the individual's neighbourhood and hence the local structure of the social network. Here we propose a simple theoretical framework to model dynamic social networks by focussing on each individual's actions instead of interactions between individuals. This eliminates the traditional dichotomy between the strategy of individuals and the structure of the population and easily complements empirical studies. As a consequence, altruists, egoists and fair types are naturally determined by the local social structures, while globally egalitarian networks or stratified structures arise. Cooperative interactions drive the emergence and shape the structure of social networks.

Via Ashish Umre
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Study maps Twitter’s information ecosystem

Study maps Twitter’s information ecosystem | Networks and Big Data | Scoop.it
New research outlines the six types of communities on the social network and what that means for communication

Via luiy, NESS
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António F Fonseca's curator insight, March 1, 7:59 AM

What community do you belong to?

Eli Levine's curator insight, March 1, 4:24 PM

Indeed, we each live in our own world, not in the real world per se.

 

Some, however, have a more accurate understanding of the real world and are willing to acknowledge their shortcomings.

 

The others, who are less inclined to explore and are more focused on their own self-production, just happen to be known as conservative in our culture.  Hence, they area always hindered from perceiving the real world in the strictest of senses, and are not likely to change in light of new information received from the outside world.

 

Non-adapting humans will equal a dead and dying species.  It's a shame, though, that we can be dragged down by them for our lack of effective effort and action.

 

Sad.

 

Think about it.

Fàtima Galan's curator insight, March 3, 2:44 AM

"The topographical "maps" of these communities, generated by Pew using the data visualization tool NodeXL, aren’t just maps of relationships. They represent the channels of information in Twitter’s vast ecosystem, the roads and throughways, stoops and street corners in each topical neighborhood where users congregate and swap news and anecdotes."

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Study uncovers six basic types of Twitter conversations

Study uncovers six basic types of Twitter conversations | Networks and Big Data | Scoop.it
Researchers say there are six structures for most conversations on Twitter, ranging from polarized debates to community clusters.

Via NESS
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Eli Levine's curator insight, February 23, 1:05 PM

This is just plain interesting.

 

How often we talk, and how little we actually have to say.

 

Think about it.

António F Fonseca's curator insight, March 1, 1:23 PM

I've already study this.

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Is this the real secret to Samsung’s success?

Is this the real secret to Samsung’s success? | Networks and Big Data | Scoop.it
Networking helps people progress at work and companies that build partnerships also do better, according to INSEAD academics.
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Efficient discovery of overlapping communities in massive networks

Efficient discovery of overlapping communities in massive networks | Networks and Big Data | Scoop.it

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.


Via Claudia Mihai
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ComplexInsight's curator insight, December 31, 2013 4:02 AM

Network visualization tools like Gephi and analysis tools like SNAP are becoming essential components in understanding, mapping and comprehending inter-relating networks and network processes. This is a good paper that gives insight into appliying networking analysis tools to identify otherwise hidden community structures in apparhently disconnected or partially connected sets which will be hugely important in large scale network analysis.

Investors Europe Stock Brokers's curator insight, Today, 2:14 AM

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Telecommunications data show civic dividing lines in major countries

Telecommunications data show civic dividing lines in major countries | Networks and Big Data | Scoop.it

Many residents of Britain, Italy, and Belgium imagine there to be a kind of north-south divide in their countries, marking a barrier between different social groups and regional characteristics. Now a new study by MIT researchers reveals that such divides can be seen in the patterns of communication in those countries and others.

 


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What Big Data Means For Social Science

We've known big data has had big impacts in business, and in lots of prediction tasks. I want to understand, what does big data mean for what we do for science? Specifically, I want to think about the following context:  You have a scientist who has a hypothesis that they would like to test, and I want to think about how the testing of that hypothesis might change as data gets bigger and bigger. So that's going to be the rule of the game. Scientists start with a hypothesis and they want to test it; what's going to happen?

 


Via Alessandro Cerboni, NESS, Complexity Digest
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Connection, Connection, Connection…

There are approximately 86 billion neurons in the human brain. Over the past decades, we have made enormous progress in understanding their molecular, genetic, and structural makeup as well as their function. However, the real power of the central nervous system lies in the smooth coordination of large numbers of neurons. Neurons are thus organized on many different scales, from small microcircuits and assemblies all the way to regional brain networks. To interact effectively on all these levels, neurons, nuclei, cortical columns, and larger areas need to be connected. The study of neuronal connectivity has expanded rapidly in past years. Large research groups have recently joined forces and formed consortia to tackle the difficult problems of how to experimentally investigate connections in the brain and how to analyze and make sense of the enormous amount of data that arises in the process.
This year's neuroscience special issue is devoted to general and also several more specific aspects of research on connectivity in the brain. We invited researchers to review the most recent progress in their fields and to provide us with an outlook on what the future may hold in store.

 

Connection, Connection, Connection…
Peter Stern

Science 1 November 2013:
Vol. 342 no. 6158 p. 577
http://dx.doi.org/10.1126/science.342.6158.577


Via Complexity Digest, Eugene Ch'ng
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Dirk Helbing: A Planetary Nervous System, and What to Do with It

10th ECCO / GBI seminar series (2013-2014) A Planetary Nervous System, and What to Do with It Part I - Seminar October 24, 2013, Brussels


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L'analisi di rete dell'organizzazione

L'analisi di rete dell'organizzazione | Networks and Big Data | Scoop.it
L'analisi di rete dell'organizzazione Tradizionalmente il management descrive ed analizza la catena di comando facendo ricorso alla struttura organizzativa formale. In realtà l’organizzazione emerg...
Complexity Institute's insight:

L’organizzazione di una comunità emerge da un sistema di reti relazionali intersoggettive, il cui funzionamento dipende dal governo di una serie di leggi naturali che le accomuna e dalle intenzioni manifestate dalle persone che le compongono.

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Community detection: effective evaluation on large social networks

Community detection: effective evaluation on large social networks | Networks and Big Data | Scoop.it

Abstract: While many recently proposed methods aim to detect network communities in large datasets, such as those generated by social media and telecommunications services, most evaluation (i.e. benchmarking) of this research is based on small, hand-curated datasets. We argue that these two types of networks differ so significantly that, by evaluating algorithms solely on the smaller networks, we know little about how well they perform on the larger datasets. Recent work addresses this problem by introducing social network datasets annotated with meta-data that is believed to approximately indicate a ‘ground truth’ set of network communities. While such efforts are a step in the right direction, we find this meta-data problematic for two reasons. First, in practice, the groups contained in such meta-data may only be a subset of a network's communities. Second, while it is often reasonable to assume that meta-data is related to network communities in some way, we must be cautious about assuming that these groups correspond closely to network communities. Here, we consider these difficulties and propose an evaluation scheme based on a classification task that is tailored to deal with them.


Via Claudia Mihai, Eric L Berlow
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Eric L Berlow's curator insight, October 17, 2013 11:29 AM

Interesting evaluation of community detection algorithms in large networks.  Most have only been validated for small, hand-curated networks.

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Biology's Big Problem: There's Too Much Data to Handle - Wired Science

Biology's Big Problem: There's Too Much Data to Handle - Wired Science | Networks and Big Data | Scoop.it
Biology’s Big Problem: There’s Too Much Data to Handle http://t.co/salfIl5BLr
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TEKsystems Big Data Survey Findings (Infographic)

TEKsystems Big Data Survey Findings (Infographic) | Networks and Big Data | Scoop.it
90 percent of IT leaders and 84 percent of IT professionals believe investments of time, money and resources into Big Data initiatives are worthwhile However, only 14 percent of IT leaders report B...
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Detecting Communities Based on Network Topology

Detecting Communities Based on Network Topology | Networks and Big Data | Scoop.it

Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.

  


Via Ashish Umre
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Ivon Prefontaine's curator insight, July 26, 6:54 PM

Community is a more complex and organic organizing than teams. Teams are inherently hierarchical with predetermined goals. Communities are fluid and the goals are continuously being negotiated. Schools and classrooms are better served to be thought of as communities with overlapping qualities and permeable boundaries with other communities.

Eli Levine's curator insight, July 29, 6:42 PM

A useful tool for policy making, because it helps identify communities and how they interact to form super-communities.

 

The essence of mapping the polity and the public, socially, economically, technologically, and infrastrucutrally.

 

Think about it.

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What Fuels the Most Influential Tweets?

What Fuels the Most Influential Tweets? | Networks and Big Data | Scoop.it
The number of followers you have and the exact wording matter less than you think. What makes a difference is having the right message for the right people.

Via luiy, NESS
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luiy's curator insight, February 22, 7:58 AM

"Influence" doesn't necessarily mean what you think it does. In the age of the social-media celebrity, a glut of Twitter followers or particularly pugnacious sampling of pithy updates are often the hallmarks of an influencer. But new research suggests that influence is situational at best: as people compete for the attention of the broader online ecosystem, the relevance of your message to the existing conversation of those around you trumps any innate "power" a person may have.

 

.... According to co-author Vespignani, having millions of followers does not denote an important message. Rather, the messages with the most immediate relevance tend to have a higher probability of resonating within a certain network than others. Think of it as "survival of the fittest" for information: those tweets that capture the most attention, whether related to a major geopolitical or news event or a particular interest, are likely to persist longer. This competition sounds bad, but it's generally good for messages in general: thousands of tweets about Japan's 2011 earthquake or the ongoing conflict in Syria don't cancel each other out, but help refocus the attention of the wider Twitter audience on those issues, which in turn provides an added lift to individual messages over other off-topic ones.

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What Big Data Means For Social Science

We've known big data has had big impacts in business, and in lots of prediction tasks. I want to understand, what does big data mean for what we do for science? Specifically, I want to think about the following context:  You have a scientist who has a hypothesis that they would like to test, and I want to think about how the testing of that hypothesis might change as data gets bigger and bigger. So that's going to be the rule of the game. Scientists start with a hypothesis and they want to test it; what's going to happen?

 


Via Alessandro Cerboni, NESS, Complexity Digest, Roger D. Jones, PhD
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Mathematical Formulation of Multilayer Networks

Mathematical Formulation of Multilayer Networks | Networks and Big Data | Scoop.it

Describing a social network based on a particular type of human social interaction, say, Facebook, is conceptually simple: a set of nodes representing the people involved in such a network, linked by their Facebook connections. But, what kind of network structure would one have if all modes of social interactions between the same people are taken into account and if one mode of interaction can influence another? Here, the notion of a “multiplex” network becomes necessary. Indeed, the scientific interest in multiplex networks has recently seen a surge. However, a fundamental scientific language that can be used consistently and broadly across the many disciplines that are involved in complex systems research was still missing. This absence is a major obstacle to further progress in this topical area of current interest. In this paper, we develop such a language, employing the concept of tensors that is widely used to describe a multitude of degrees of freedom associated with a single entity.

Our tensorial formalism provides a unified framework that makes it possible to describe both traditional “monoplex” (i.e., single-type links) and multiplex networks. Each type of interaction between the nodes is described by a single-layer network. The different modes of interaction are then described by different layers of networks. But, a node from one layer can be linked to another node in any other layer, leading to “cross talks” between the layers. High-dimensional tensors naturally capture such multidimensional patterns of connectivity. Having first developed a rigorous tensorial definition of such multilayer structures, we have also used it to generalize the many important diagnostic concepts previously known only to traditional monoplex networks, including degree centrality, clustering coefficients, and modularity.

We think that the conceptual simplicity and the fundamental rigor of our formalism will power the further development of our understanding of multiplex networks.

 


Via Claudia Mihai
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Peculiar traffic routes suggest hijacking headaches

Peculiar traffic routes suggest hijacking headaches | Networks and Big Data | Scoop.it

Findings from Internet intelligence company Renesys sound an alert to a hijacking practice in the form of traffic misdirection on the Internet. A November 19 blog on the Renesys site has since caught the attention of a wider press: "Who is sending Internet traffic on long, strange trips?" asked a headline in The Christian Science Monitor earlier this month. The Renesys blog author, Jim Cowie, Chief Technology Officer, said that "We have actually observed live Man-In-the-Middle (MITM) hijacks on more than 60 days so far this year." He said about 1,500 individual IP blocks have been hijacked in events lasting from minutes to days by attackers working from various countries. Simply put, data to and from finance firms, net phone services and governments was re-routed in several attacks this year. As Michael Mimosa of Threatpost noted, "Attackers are accessing routers running on the border gateway protocol (BGP) and injecting additional hops that redirect large blocks of Internet traffic to locations where it can be monitored and even manipulated before being sent to its intended destination.


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A Scalable Heuristic for Viral Marketing Under the Tipping Model

A Scalable Heuristic for Viral Marketing Under the Tipping Model | Networks and Big Data | Scoop.it

In a "tipping" model, each node in a social network, representing anindividual, adopts a property or behavior if a certain number of his incomingneighbors currently exhibit the same. In viral marketing, a key problem is toselect an initial "seed" set from the network such that the entire networkadopts any behavior given to the seed. Here we introduce a method for quicklyfinding seed sets that scales to very large networks. Our approach finds a setof nodes that guarantees spreading to the entire network under the tippingmodel. After experimentally evaluating 31 real-world networks, we found thatour approach often finds seed sets that are several orders of magnitude smallerthan the population size and outperform nodal centrality measures in mostcases. In addition, our approach scales well - on a Friendster social networkconsisting of 5.6 million nodes and 28 million edges we found a seed set inunder 3.6 hours. Our experiments also indicate that our algorithm providessmall seed sets even if high-degree nodes are removed. Lastly, we find thathighly clustered local neighborhoods, together with dense network-widecommunity structures, suppress a trend's ability to spread under the tippingmodel.


Via Bernard Ryefield
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Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook

A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organize our analysis around a basic question: given all the connections among a person's friends, can you recognize his or her romantic partner from the network structure alone? Using data from a large sample of Facebook users, we find that this task can be accomplished with high accuracy, but doing so requires the development of a new measure of tie strength that we term `dispersion' --- the extent to which two people's mutual friends are not themselves well-connected. The results offer methods for identifying types of structurally significant people in on-line applications, and suggest a potential expansion of existing theories of tie strength.

 

Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook
Lars Backstrom, Jon Kleinberg

http://arxiv.org/abs/1310.6753


Via Complexity Digest, Eugene Ch'ng
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Trust and Manipulation in Social Networks

"We investigate the role of manipulation in a model of opinion formation where agents have opinions about some common question of interest. Agents repeatedly communicate with their neighbors in the social network, can exert some effort to manipulate the trust of others, and update their opinions taking weighted averages of neighbors' opinions. The incentives to manipulate are given by the agents' preferences. We show that manipulation can modify the trust structure and lead to a connected society, and thus, make the society reaching a consensus. Manipulation fosters opinion leadership, but the manipulated agent may even gain influence on the long-run opinions. In sufficiently homophilic societies, manipulation accelerates (slows down) convergence if it decreases (increases) homophily. Finally, we investigate the tension between information aggregation and spread of misinformation. We find that if the ability of the manipulating agent is weak and the agents underselling (overselling) their information gain (lose) overall influence, then manipulation reduces misinformation and agents converge jointly to more accurate opinions about some underlying true state."


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CMWL - Presentazione

CMWL - Presentazione | Networks and Big Data | Scoop.it

In tempi di elevata incertezza è necessario conoscere ed adottare nuove tecniche di governo e di co-solution delle problematiche organizzative. Le organizzazioni si comportano come una rete neuronale. Le persone sono come i neuroni che si collegano tra di loro accendendo pensieri ed emozioni: dalla loro interazione emergono idee, azioni, decisioni, progetti. Non sempre però ci è visibile ciò che sta effettivamente accadendo nella rete organizzativa. Si possono fare errori, scegliere strade sbagliate o impiegare tempi lunghi per decisioni che potrebbero essere più rapide, più consapevoli, meno onerose e più efficaci.

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For Preventing Disease, Data Are the New Drugs

For Preventing Disease, Data Are the New Drugs | Networks and Big Data | Scoop.it

One of the world’s most advanced data mining projects applies this same kind of analysis to cancer. Ilya Shmulevich, a lead genomicist who directs a Genome Data Analysis Center at the National Institutes of Health’s The Cancer Genome Atlas, says the project was born out of a shared frustration among cancer researchers at being forced, by a dearth of data, to study cancer one defective gene at a time, even while suspecting that the disease is actually the result of many genomic malfunctions, all happening at once.

 

http://nautil.us/issue/6/secret-codes/for-preventing-disease-data-are-the-new-drugs


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Dmitry Alexeev's curator insight, October 26, 2013 6:37 AM

dataawareness is a new skill for scientists

 

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Significant Scales in Community Structure

Significant Scales in Community Structure | Networks and Big Data | Scoop.it
Many complex networks show signs of modular structure, uncovered by community detection. Although many methods succeed in revealing various partitions, it remains difficult to detect at what scale some partition is significant.

Via Claudia Mihai, Eric L Berlow
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Eric L Berlow's curator insight, October 17, 2013 11:48 AM

Evaluating the statistical significance of modules within networks identified by community detection algorithms

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TEDxAmsterdam » Superfluous is the goal: The surprising seeds of a big-data revolution in healthcare

Superfluous is the goal: The surprising seeds of a #bigdata revolution in #healthcare http://t.co/FozdsCdtRB
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