networks and network weaving
9.3K views | +6 today
Follow
networks and network weaving
How networks can transform our world
Curated by june holley
Your new post is loading...
Your new post is loading...
Rescooped by june holley from CxBooks
Scoop.it!

The Automation of Society is Next: How to Survive the Digital Revolution

The book that may have saved democracy [Version 1.0]


1 THE DIGITAL SOCIETY A better future or worse?
2 COMPLEXITY TIME BOMB When systems get out of control
3 SOCIAL FORCES Revealing the causes of success and disaster
4 GOOGLE AS GOD? The dangerous promise of Big Data
5 GENIE OUT OF THE BOTTLE Major socio-economic shifts ahead
6 DIGITALLY ASSISTED SELF-ORGANIZATION Making the invisible hand work
7 HOW SOCIETY WORKS Social order by self-organization
8 NETWORKED MINDS Where human evolution is heading
9 ECONOMY 4.0 A participatory market society is born
10 THE SELF-ORGANIZING SOCIETY Taking the future in our hands


The Automation of Society is Next: How to Survive the Digital Revolution

Dirk Helbing

https://www.researchgate.net/publication/283206311_The_Automation_of_Society_is_Next_How_to_Survive_the_Digital_Revolution 


Via Complexity Digest
more...
Rescooped by june holley from CxAnnouncements
Scoop.it!

The largest study of creative styles in history leverages a network approach to multidimensional scaling.

The largest study of creative styles in history leverages a network approach to multidimensional scaling. | networks and network weaving | Scoop.it

Hacking Creativity, led by the Red Bull High Performance group, is the largest study of creative style in history. And now everyone can participate in this groundbreaking effort to unlock the secrets of the creative process. Take our quick survey: It’s not a test of how creative you are, but rather a profile of how you create.

Discover which creative habits you share with a diverse group of 500 global innovators. Are you a multitasker or do you prefer to focus on one project at a time? Do you work best alone or in a more collaborative environment? Find inspiration from those trailblazers following similar paths to creativity and learn how to boost your own creative firepower.


http://hackingcreativity.com/


Via Complexity Digest
more...
Rescooped by june holley from Talks
Scoop.it!

Francis Heylighen: Towards an Intelligent Network for Matching Offer and Demand

Towards an Intelligent Network for Matching Offer and Demand: from the sharing economy to the Global Brain
Francis Heylighen

December 4, 2015, Brussels

Workshop on Offer Networks
http://onet.globalbraininstitute.org
The Global Brain Institute
Vrije Universiteit Brussel


https://www.youtube.com/watch?v=DG0N1yiJ6lw


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Ecological Multilayer Networks: A New Frontier for Network Ecology

Networks provide a powerful approach to address myriad phenomena across ecology. Ecological systems are inherently 'multilayered'. For instance, species interact with one another in different ways and those interactions vary spatiotemporally. However, ecological networks are typically studied as ordinary (i.e., monolayer) networks. 'Multilayer networks' are currently at the forefront of network science, but ecological multilayer network studies have been sporadic and have not taken advantage of rapidly developing theory. Here we present the latest concepts and tools of multilayer network theory and discuss their application to ecology. This novel framework for the study of ecological multilayer networks encourages ecologists to move beyond monolayer network studies and facilitates ways for doing so. It thereby paves the way for novel, exciting research directions in network ecology.


Ecological Multilayer Networks: A New Frontier for Network Ecology
Shai Pilosof, Mason A. Porter, Sonia Kéfi

http://arxiv.org/abs/1511.04453


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

The Evolution of Wikipedia's Norm Network

Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. Here, we present the development of the network of norms that sustain the online social system Wikipedia. We do so using a network of pages that establish, describe, and interpret the society's norms. Fifteen years of high-resolution data allow us to study how this network evolves over time. Despite Wikipedia's reputation for ad hoc governance, we find that its normative evolution is highly conservative. The earliest users create norms that dominate the network and persist over time. These core norms govern both content and interpersonal interactions, using abstract principles such as neutrality, verifiability, and assume good faith. As the network grows, norm neighborhoods decouple topologically from each other, while increasing in semantic coherence. Taken together, these results suggest that the evolution of Wikipedia's norm network is akin to bureaucratic systems that predate the information age.


The Evolution of Wikipedia's Norm Network
Bradi Heaberlin, Simon DeDeo

http://arxiv.org/abs/1512.01725


Via Complexity Digest
more...
No comment yet.
Scooped by june holley
Scoop.it!

So You Want to Host a Web Meeting? A Resource

So You Want to Host a Web Meeting? A Resource | networks and network weaving | Scoop.it
A long time ago in a planet far far away, a group of people asked if I could share some of my web meeting tips. I have a lot of tips, most of them learned from many many colleagues from all over, b…
more...
No comment yet.
Scooped by june holley
Scoop.it!

Why communities of color are getting frustrated with Collective Impact

Why communities of color are getting frustrated with Collective Impact | networks and network weaving | Scoop.it
A while ago I wrote “Collective Impact: Resistance is Futile,” detailing the frustrations of CI and comparing it to The Borg on Star Trek. “Controlled by a hive mind that neutralizes any sort of in...
more...
No comment yet.
Scooped by june holley
Scoop.it!

Imagining social movements: from networks to dynamic systems

Imagining social movements: from networks to dynamic systems | networks and network weaving | Scoop.it
We must move to an understanding of networks as constantly changing and dynamic; no two 'links' are alike.
more...
Diego Mora's curator insight, December 1, 2015 9:35 AM

Nuevas comprensiones de los movimientos sociales, de redes a sistemas dinámicos.

Liz Rykert's curator insight, December 2, 2015 8:57 AM

Thanks June Holley for this scoop. It is a concise analysis of networks in the context of social movements in Europe. 

Francisco Restivo's curator insight, December 2, 2015 3:42 PM

Really interesting.

Scooped by june holley
Scoop.it!

What We’ve Learned After a Decade of Climate Funding, and What We’re Doing Instead — Medium

What We’ve Learned After a Decade of Climate Funding, and What We’re Doing Instead — Medium | networks and network weaving | Scoop.it
Philanthropy isn’t necessarily known for making long-term commitments. It is, however, known for making big announcements. At the Chorus…
more...
No comment yet.
Rescooped by june holley from Peer2Politics
Scoop.it!

Contentious Moments at the Platform Cooperativism conference | P2P Foundation

Contentious Moments at the Platform Cooperativism conference | P2P Foundation | networks and network weaving | Scoop.it
Excerpted from a conference review by Jay Cassano: “Some of the most exciting developments came from the self-organized breakout sessions, such as one on where@ — a proposal for a secure location-sharing app for activists. Other workshops focused on alternative currencies, ethical user interface design, and data science. The most contentious moments of the gathering …

Via jean lievens
more...
No comment yet.
Rescooped by june holley from Talks
Scoop.it!

How to Increase Systemic Resilience in an Information-Rich World


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Unbiased metrics of friends’ influence in multi-level networks

The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of their friends. We demonstrate that the currently existing metrics of friends’ influence are biased by the presence of highly popular items in the data, and as a result can lead to an illusion of friends influence where there is none. We correct for this bias and develop three metrics that allow to distinguish the influence of friends from the effects of item popularity, and apply the metrics on real datasets. We use a simple network model based on the influence of friends and preferential attachment to illustrate the performance of our metrics at different levels of friends’ influence.


Unbiased metrics of friends’ influence in multi-level networks
Alexandre Vidmer, Matúš Medo and Yi-Cheng Zhang

EPJ Data Science 2015, 4:20  http://dx.doi.org/10.1140/epjds/s13688-015-0057-x ;


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting

Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline “real” world. As social media data can be obtained in near real-time and at low cost, it is often used for “now-casting” indices such as levels of flu activity or unemployment. The term “social sensing” is often used in this context to describe the idea that users act as “sensors”, publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a “one tweet, one vote” fashion by simply counting. At the same time, researchers readily admit that social media users are not a perfect representation of the actual population. Additionally, users differ in the amount of details of their personal lives that they reveal. Intuitively, it should be possible to improve now-casting by assigning different weights to different user groups.
In this paper, we ask “How does social sensing actually work?” or, more precisely, “Whom should we sense-and whom not-for optimal results?”. We investigate how different sampling strategies affect the performance of now-casting of two common offline indices: flu activity and unemployment rate. We show that now-casting can be improved by (1) applying user filtering techniques and (2) selecting users with complete profiles. We also find that, using the right type of user groups, now-casting performance does not degrade, even when drastically reducing the size of the dataset. More fundamentally, we describe which type of users contribute most to the accuracy by asking if “babblers are better”. We conclude the paper by providing guidance on how to select better user groups for more accurate now-casting.


Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting
Jisun An and Ingmar Weber

EPJ Data Science 2015, 4:22  http://dx.doi.org/10.1140/epjds/s13688-015-0058-9 


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Measuring online social bubbles

Measuring online social bubbles | networks and network weaving | Scoop.it

Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information.


Measuring online social bubbles
Dimitar Nikolov, Diego F.M. Oliveira, Alessandro Flammini, Filippo Menczer

http://dx.doi.org/10.7717/peerj-cs.38 ;


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting

Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline “real” world. As social media data can be obtained in near real-time and at low cost, it is often used for “now-casting” indices such as levels of flu activity or unemployment. The term “social sensing” is often used in this context to describe the idea that users act as “sensors”, publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a “one tweet, one vote” fashion by simply counting. At the same time, researchers readily admit that social media users are not a perfect representation of the actual population. Additionally, users differ in the amount of details of their personal lives that they reveal. Intuitively, it should be possible to improve now-casting by assigning different weights to different user groups.
In this paper, we ask “How does social sensing actually work?” or, more precisely, “Whom should we sense-and whom not-for optimal results?”. We investigate how different sampling strategies affect the performance of now-casting of two common offline indices: flu activity and unemployment rate. We show that now-casting can be improved by (1) applying user filtering techniques and (2) selecting users with complete profiles. We also find that, using the right type of user groups, now-casting performance does not degrade, even when drastically reducing the size of the dataset. More fundamentally, we describe which type of users contribute most to the accuracy by asking if “babblers are better”. We conclude the paper by providing guidance on how to select better user groups for more accurate now-casting.


Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting
Jisun An and Ingmar Weber

EPJ Data Science 2015, 4:22  http://dx.doi.org/10.1140/epjds/s13688-015-0058-9 


Via Complexity Digest
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

The ontogeny of fairness in seven societies

An analysis of when children develop a sense of fairness (receiving less or more than a peer) is compared across seven different societies; aversion to receiving less emerges early in childhood in all societies, whereas aversion to receiving more emerges later in childhood and only in three of the seven societies studied.


The ontogeny of fairness in seven societies
• P. R. Blake, K. McAuliffe, J. Corbit, T. C. Callaghan, O. Barry, A. Bowie, L. Kleutsch, K. L. Kramer, E. Ross, H. Vongsachang, R. Wrangham & F. Warneken

Nature 528, 258–261 (10 December 2015) http://dx.doi.org/10.1038/nature15703 ;


Via Complexity Digest
more...
No comment yet.
Scooped by june holley
Scoop.it!

Distributed Teams — the Next Big Economic Sea-Change? — What’s The Future of Work? — Medium

Distributed Teams - the Next Big Economic Sea-Change? - What's The Future of Work? - Medium
more...
No comment yet.
Scooped by june holley
Scoop.it!

Deepening Network Practice for Social Change

Deepening Network Practice for Social Change | networks and network weaving | Scoop.it
Last week, we held an internal learning session for staff and affiliates entitled "Advancing Equitable Networks." IISC Affiliate Kiara Nagel and I presented some thoughts about our ever evolving pr...
more...
No comment yet.
Rescooped by june holley from operationalizing complexity
Scoop.it!

The New Laws of Explosive Networks

The New Laws of Explosive Networks | networks and network weaving | Scoop.it

Researchers are uncovering the hidden laws that reveal how the Internet grows, how viruses spread, and how financial bubbles burst.

 

https://www.quantamagazine.org/20150714-explosive-percolation-networks/ ;


Via Complexity Digest, Bill Aukett
more...
No comment yet.
Scooped by june holley
Scoop.it!

Does London need a Radical Assembly?

Does London need a Radical Assembly? | networks and network weaving | Scoop.it
Could there be a better way to organise progressive resources?
more...
No comment yet.
Scooped by june holley
Scoop.it!

Funding the Movement for Black Lives | Responsive Philanthropy Article Archive

Funding the Movement for Black Lives | Responsive Philanthropy Article Archive | networks and network weaving | Scoop.it
more...
No comment yet.
Rescooped by june holley from Networks and Graphs
Scoop.it!

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

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

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

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

 

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

http://arxiv.org/abs/1511.01238 ;


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

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

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

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

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

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

 

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

Scooped by june holley
Scoop.it!

Complex Systems Science: Where Does It Come From and Where is It Going To? | NECSI

Complex Systems Science: Where Does It Come From and Where is It Going To? | NECSI | networks and network weaving | Scoop.it
more...
No comment yet.
Rescooped by june holley from Papers
Scoop.it!

Online social networks and offline protest

Large-scale protests occur frequently and sometimes overthrow entire political systems. Meanwhile, online social networks have become an increasingly common component of people’s lives. We present a large-scale longitudinal study that connects online social media behaviors to offline protest. Using almost 14 million geolocated tweets and data on protests from 16 countries during the Arab Spring, we show that increased coordination of messages on Twitter using specific hashtags is associated with increased protests the following day. The results also show that traditional actors like the media and elites are not driving the results. These results indicate social media activity correlates with subsequent large-scale decentralized coordination of protests, with important implications for the future balance of power between citizens and their states.


Online social networks and offline protest
Zachary C Steinert-Threlkeld, Delia Mocanu, Alessandro Vespignani and James Fowler

EPJ Data Science 2015, 4:19  http://dx.doi.org/10.1140/epjds/s13688-015-0056-y 


Via Complexity Digest
more...
No comment yet.