🔖 An Experimental Study of the Small World Problem by Jeffrey Travers and Stanley Milgram | Sociometry

Bookmarked An Experimental Study of the Small World Problem by Jeffrey Travers and Stanley Milgram (Sociometry | American Sociological Association)
Arbitrarily selected individuals (N=296) in Nebraska and Boston are asked to generate acquaintance chains to a target person in Massachusetts, employing "the small world method" (Milgram, 1967). Sixty-four chains reach the target person. Within this group the mean number of intermediaries between starters and targets is 5.2. Boston starting chains reach the target person with fewer intermediaries than those starting in Nebraska; subpopulations in the Nebraska group do not differ among themselves. The funneling of chains through sociometric "stars" is noted, with 48 per cent of the chains passing through three persons before reaching the target. Applications of the method to studies of large scale social structure are discussed.

h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Economic Action and Social Structure: The Problem of Embeddedness | Mark Granovetter | American Journal of Sociology: Vol 91, No 3

Bookmarked Economic Action and Social Structure: The Problem of Embeddedness by Mark Granovetter (American Journal of Sociology)
How behavior and institutions are affected by social relations is one of the classic questions of social theory. This paper concerns the extent to which economic action is embedded in structures of social relations, in modern industrial society. Although the usual neoclasical accounts provide an "undersocialized" or atomized-actor explanation of such action, reformist economists who attempt to bring social structure back in do so in the "oversocialized" way criticized by Dennis Wrong. Under-and oversocialized accounts are paradoxically similar in their neglect of ongoing structures of social relations, and a sophisticated account of economic action must consider its embeddedness in such structures. The argument in illustrated by a critique of Oliver Williamson's "markets and hierarchies" research program.

h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

🔖 Science and Complexity by Warren Weaver

Bookmarked Science and complexity by Warren Weaver (American Scientist, 36: 536-544)

h/t Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. Hidalgo (Applied Network Science | SpringerLink)

👓 Disconnected, fragmented, or united? a trans-disciplinary review of network science | Applied Network Science | César A. Hidalgo

Read Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.

Highlights, Quotes, Annotations, & Marginalia

Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms.  

August 25, 2018 at 10:18PM

Science and Complexity (Weaver 1948); explained the three eras that according to him defined the history of science. These were the era of simplicity, disorganized complexity, and organized complexity. In the eyes of Weaver what separated these three eras was the development of mathematical tools allowing scholars to describe systems of increasing complexity.  

August 25, 2018 at 10:19PM

Problems of disorganized complexity are problems that can be described using averages and distributions, and that do not depend on the identity of the elements involved in a system, or their precise patterns of interactions. A classic example of a problem of disorganized complexity is the statistical mechanics of Ludwig Boltzmann, James-Clerk Maxwell, and Willard Gibbs, which focuses on the properties of gases.  

August 25, 2018 at 10:20PM

Soon after Weaver’s paper, biologists like Francois Jacob (Jacob and Monod 1961), (Jacob et al. 1963) and Stuart Kaufmann (Kauffman 1969), developed the idea of regulatory networks. Mathematicians like Paul Erdos and Alfred Renyi, advanced graph theory (Erdős and Rényi 1960) while Benoit Mandelbrot worked on Fractals (Mandelbrot and Van Ness 1968), (Mandelbrot 1982). Economists like Thomas Schelling (Schelling 1960) and Wasily Leontief (Leontief 1936), (Leontief 1936), respectively explored self-organization and input-output networks. Sociologists, like Harrison White (Lorrain and White 1971) and Mark Granovetter (Granovetter 1985), explored social networks, while psychologists like Stanley Milgram (Travers and Milgram 1969) explored the now famous small world problem.   

Some excellent references
August 25, 2018 at 10:24PM

First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.  

August 25, 2018 at 10:27PM

Links are the essence of networks. So I will start this review by comparing the mechanisms used by natural and social scientists to explain link formation.  

August 25, 2018 at 10:32PM

When connecting the people that acted in the same movie, natural scientists do not differentiate between people in leading or supporting roles.  

But they should because it’s not often the case that these are relevant unless they are represented by the same agent or agency.
August 25, 2018 at 10:51PM

For instance, in the study of mobile phone networks, the frequency and length of interactions has often been used as measures of link weight (Onnela et al. 2007), (Hidalgo and Rodriguez-Sickert 1008), (Miritello et al. 2011).  

And they probably shouldn’t because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don’t care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.
August 25, 2018 at 10:52PM

Social scientists explain link formation through two families of mechanisms; one that finds it roots in sociology and the other one in economics. The sociological approach assumes that link formation is connected to the characteristics of individuals and their context. Chief examples of the sociological approach include what I will call the big three sociological link-formation hypotheses. These are: shared social foci, triadic closure, and homophily.  

August 25, 2018 at 10:55PM

The social foci hypothesis predicts that links are more likely to form among individuals who, for example, are classmates, co-workers, or go to the same gym (they share a social foci). The triadic closure hypothesis predicts that links are more likely to form among individuals that share “friends” or acquaintances. Finally, the homophily hypothesis predicts that links are more likely to form among individuals who share social characteristics, such as tastes, cultural background, or physical appearance (Lazarsfeld and Merton 1954), (McPherson et al. 2001).  

definitions of social foci, triadic closure, and homophily within network science.
August 26, 2018 at 11:39AM

Yet, strategic games look for equilibrium in the formation and dissolution of ties in the context of the game theory advanced first by (Von Neumann et al. 2007), and later by (Nash 1950).  

August 25, 2018 at 10:58PM

Preferential attachment is the idea that connectivity begets connectivity.  

August 25, 2018 at 10:59PM

Preferential attachment is an idea advanced originally by the statisticians John Willis and Udny Yule in (Willis and Yule 1922), but has been rediscovered numerous times during the twentieth century.  

August 25, 2018 at 11:00PM

Rediscoveries of this idea in the twentieth century include the work of (Simon 1955) (who did cite Yule), (Merton 1968), (Price 1976) (who studied citation networks), and (Barabási and Albert 1999), who published the modern reference for this model, which is now widely known as the Barabasi-Albert model.  

August 25, 2018 at 11:01PM

preferential attachment. In the eyes of the social sciences, however, understanding which of all of these hypotheses drives the formation of the network is what one needs to explore.  

For example what drives attachment of political candidates?
August 26, 2018 at 08:15AM

Finally it is worth noting that trust, through the theory of social capital, has been connected with long-term economic growth—even though these results are based on regressions using extremely sparse datasets.  

And this is an example of how Trump is hurting the economy.
August 26, 2018 at 08:33AM

Nevertheless, the evidence suggests that social capital and social institutions are significant predictors of economic growth, after controlling for the effects of human capital and initial levels of income (Knack and Keefer 1997), (Knack 2002).4 So trust is a relevant dimension of social interactions that has been connected to individual dyads, network formation, labor markets, and even economic growth.  

August 26, 2018 at 08:35AM

Social scientist, on the other hand, have focused on what ties are more likely to bring in new information, which are primarily weak ties (Granovetter 1973), and on why weak ties bring new information (because they bridge structural holes (Burt 2001), (Burt 2005)).  

August 26, 2018 at 09:45AM

heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).  

August 26, 2018 at 09:49AM

These results, however, have also been challenged by human experiments finding no such effect (Gracia-Lázaro et al. 2012). The study of cooperation in networks has also been performed in dynamic settings, where individuals are allowed to cut ties (Wang et al. 2012), promoting cooperation, and are faced with different levels of knowledge about the reputation of peers in their network (Gallo and Yan 2015). Moreover, cooperating behavior has seen to spread when people change the networks where they participate in (Fowler and Christakis 2010).  

Open questions
August 26, 2018 at 09:50AM

References

1.
Hidalgo CA. Disconnected, fragmented, or united? a trans-disciplinary review of network science. ANS. 2016;1(1). doi:10.1007/s41109-016-0010-3

🔖 NetSci 2018 11-15 June 2018 in Paris, France

Bookmarked NetSci 2018 (NetSci 2018)
NetSci 2018, the flagship conference of the Network Science Society, aims to bring together leading researchers and practitioners working in the emerging area of network science. The conference fosters interdisciplinary communication and collaboration in network science research across computer and information sciences, physics, mathematics, statistics, the life sciences, neuroscience, environmental sciences, social sciences, finance and business, arts and design. NetSci 2018 in Paris, France will be a combination of: * An International School for students and non-experts (June 11-12, 2018) * Satellite Symposia (June 11-12, 2018) * A 3-day Conference (June 13-15, 2018) featuring research in a wide range of topics and in different formats, including keynote and invited talks, oral presentations, posters, and lightning talks.

Registration Deadlines:
February 8: Registration opens.
March 20: Registration for presenters of accepted contributions ends.
April 10: Early registration ends.
May 28: Online registration ends.

🔖 Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Bookmarked Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity (arxiv.org)
Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems, from micro- to macroscales. We present examples of how human brain imaging data is being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers, and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights critical to the neuroengineer's toolkit.

17 pages, 6 figures. Manuscript accepted to the journal Annual Review of Biomedical Engineering [1]

References

[1]
D. Bassett S., A. Khambhati N., and S. Grafton T., “Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity,” arXiv, 23-Dec-2016. [Online]. Available: https://arxiv.org/abs/1612.08059. [Accessed: 03-Jan-2017]

Network Science by Albert-László Barabási

Bookmarked Network Science by Albert-László BarabásiAlbert-László Barabási (Cambridge University Press)

I ran across a link to this textbook by way of a standing Google alert, and was excited to check it out. I was immediately disappointed to think that I would have to wait another month and change for the physical textbook to be released, but made my pre-order directly. Then with a bit of digging around, I realized that individual chapters are available immediately to quench my thirst until the physical text is printed next month.

The power of network science, the beauty of network visualization.

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

The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2016. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.

Book Contents

Personal Introduction
1. Introduction
2. Graph Theory
3. Random Networks
4. The Scale-Free Property
5. The Barabási-Albert Model
6. Evolving Networks
7. Degree Correlations
8. Network Robustness
9. Communities
10. Spreading Phenomena
Usage & Acknowledgements
About

Albert-László Barabási
on Network Science (book website)

Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network science.

Source: Cambridge University Press

The textbook is available for purchase in September 2016 from Cambridge University Press. Pre-order now on Amazon.com.

If you’re not already doing so, you should follow Barabási on Twitter.

Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science

Bookmarked Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)

Applied Network Science

Abstract

During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.