Original Article:

http://www.technologyreview.com/view/534576/how-network-science-is-changing-our-understanding-of-law/

Summary:

Konaris et al at the National Technical University of Athens in Greece expanded on existing legal network analysis by extracting “all the documents from the European Community’s legal database dating back to 1951” and organizing the texts into three subnetworks: treaties between countries, regulations and directives that are based on these treaties, and case laws that have emerged from the application of these regulations. Analysis of each subsection showed that “all were small world networks in themselves” that generally demonstrate high levels of resilience. This model produced a novel perspective because it takes into account both the temporal dynamism and hierarchical nature of European law. In addition to mapping citations (references that do not modify the target document) Konaris also represented legal bias (edit references that modify either the text or the lifecycle of the target document) and accounted for their effects over time. This revealed “a steep increase in the density of links within the network over time”. Ultimately, Konaris suggests that the clusters and related connections illustrated by this work may “help legislators determine the effect of proposed changes and improve the effectiveness of legal information retrieval”.

Paper:

http://arxiv.org/abs/1501.05237

According to a recent article in Forbes, the majority of people “spend most of their time in closed networks”. However, closed networks in the workplace, although seemingly effective for a managerial standpoint, are actually less conducive for long-term career success. Open networks promote innovation and according to this study, “simply being in an open network instead of a closed one is the best predictor of career success.”

Read more here: http://www.forbes.com/sites/michaelsimmons/2015/01/15/this-is-the-1-predictor-of-career-success-according-to-network-science/

Christopher Monterola of that A*STAR Institute of High Performance Computing in Singapore and co-researchers at and The Logistics Institute-Asia Pacific put forth a computer model that predicts the most expedient over-ground routes using real-time data uploaded by agents on the scene. This model allows users to project the “flow of goods and other relief efforts, and quantify the reachability of critical loci within a geographic area”. To create this tool, Moterola’s team “developed a procedure that automatically transforms street maps into a network of nodes (road intersections) and edges (road segments)”. By allowing for continuous updates from crowd-sourced platforms like OpenStreetMap, the model incorporates a flexibility that enables local governments to visualize multiple infrastructure destruction scenarios.

The team achieved this by comparing “two different model networks: a grid lattice common in cities and a ‘scale-free’ road network that represents a mix of urban hubs and rural spokes”. In the resulting paper, Monterola et al. discuss “the inaccuracy of assuming that road networks follow a structure similar to the more commonly studied scale-free, random, and/or grid (regular) network configurations” – contradicting conventional assumptions used in preparedness planning. Hopefully, these findings will help correct the flaws of existing practices that forestall relief to victims (like those of Typhoon Haiyan in 2013) and mitigate the loss of life and property.

News Article:
http://phys.org/news/2014-12-algorithm-automatically-quickest-calamity-affected-sites.html#jCp

Full Article:
http://www.worldscientific.com.turing.library.northwestern.edu/doi/pdf/10.1142/S0129183114500478

In recent years, the spread of information over social networks has become a global phenomenon. Scientists have begun to study how information spreads from person to person, and how individuals’ motivations can cause a cascade effect in behavior. Researchers note that often “cascading behavior in a social network is guided by an entity that wants to achieve a certain outcome, for example… a political movement might be trying to guide the success of its message in a population.”

Further research will shed light on what guides the spread of information over social networks, and how individual behaviors play a role in information trends.

Read more here: http://zeenews.india.com/news/space/the-math-of-how-decisions-cascade-in-social-networks_1528658.html

The study of the network of networks, or interdependent networks, has become increasingly important. Interdependent networks are hugely prevalent especially in food, water, and fuel delivery systems. Scientists at Northeastern, Beihang, and Bar-Ilan Universities wrote in the National Science Review that the study of network of networks is “one of the most important directions in network science”.

Read more here: http://phys.org/news/2014-12-scientists-worldwide-network-networks.html

What makes one a good group member? Is it intelligence? A recent study found that, “having a lot of intelligent people in the group did not necessarily mean that your group will have sure success.” 

According to this study, what mattered more for group success was how individuals scored on the Reading the Mind in the Eyes (RME) test. “The better the group members are in reading other people’s emotions through their eyes, the better the chances for the group’s success.”

Given some thought, the results of this study are not surprising. It certainly makes sense that a group member with the ability to be empathetic towards a fellow group member would be a better team-player than one who does not have that ability. Perhaps you might want to practice with this online RME Test before embarking on future collaborations?

Read more here: http://www.rappler.com/science-nature/ideas/science-solitaire/80143-science-group-success

This special issue of the Proceedings of the IEEE focuses on how digital technology is changing the structure and dynamics of social networks and the tools we have for studying and designing them. Three main take-home messages:

• Social media, search, and data extraction technologies are not only changing the structure and dynamics of social networks, but are also changing how controllable these systems are.

• Precision, quantitatively justified interventions into behavioral dynamics are increasingly feasible within the digital domain, permitting large-scale experiments on human behavior and social systems. This is useful and presents challenges.

• We understand the relationship between energy and information – how bits get converted to watts – for electrical circuits, but not for social networks. In biology, computational social science, and the science of social engineering, the development of a functional theory of information is a central theoretical challenge that needs to be addressed if these disciplines are to have strong foundations.

Read more here: http://mae.engr.ucdavis.edu/dsouza/Pubs/PIEEE_vol102_12.pdf