"A University of Washington professor started studying social networks to help people respond to disasters. But she got dragged down a rabbit hole of twitter-boosted conspiracy theories, and ended up mapping our political moment.
In this story, a dog introduces us to a strange creature that burrows beneath forests, building an underground network where deals are made and lives are saved (and lost) in a complex web of friendships, rivalries, and business relations. It’s a network that scientists are only just beginning to untangle and map, and it’s not only turning our understanding of forests upside down, it’s leading some researchers to rethink what it means to be intelligent.
The bitterly factious 2016 U.S. presidential election campaign was the culmination of several trends that, taken together, constitute a syndrome of chronic ailments in the body politic. Ironically, these destructive trends have accelerated just as science has rapidly improved our understanding of them and their underlying causes. But mere understanding is not sufficient to repair our politics. The challenge is to build a translational science of democracy that maintains scientific rigor while actively promoting the health of the body politic.
Michael A. Neblo, William Minozzi, Kevin M. Esterling, Jon Green, Jonathon Kingzette, David M. J. Lazer
Science 03 Mar 2017: Vol. 355, Issue 6328, pp. 914-915 DOI: 10.1126/science.aal3900
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.
The many facets of community detection in complex networks Michael T. SchaubEmail author, Jean-Charles Delvenne, Martin Rosvall and Renaud Lambiotte Applied Network Science20172:4 DOI: 10.1007/s41109-017-0023-6
David Edery, who was until recently part of the CMS staff and now works for Microsoft, has been generating some interesting discussion over on his blog, Game Tycoon, about how games might harness “the wisdom of crowds” to solve real world problems. It’s an idea he’s been promoting for some time but I only recently had a chance to read through all of his discussion. He starts by describing the growing academic interest that has been generated by James Surowiecki’s The Wisdom of Crowds and then suggesting some of the challenges of applying these concepts in a real world context: -
Hello, lover of democracy. Welcome to an exploration of how we can make democracy more wise... OUR PURPOSE The Wise Democracy Project exists to help us envision and co-create a deeply participatory culture that generates policies and activities
Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.
Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic José M. Gómez & Miguel Verdú Scientific Reports 7, Article number: 43467 (2017) doi:10.1038/srep43467
Transformation involves fundamental change, which in the context of sustainability, requires radical, systemic shifts in values and beliefs, patterns of social behavior, and multilevel governance and management regimes (Olsson et al. 2014).
"A forest is much more than what you see," says ecologist Suzanne Simard. Her 30 years of research in Canadian forests have led to an astounding discovery -- trees talk, often and over vast distances. Learn more about the harmonious yet complicated social lives of trees and prepare to see the natural world with new eyes.
How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.
Godoy A, Tabacof P, Von Zuben FJ (2017) The role of the interaction network in the emergence of diversity of behavior. PLoS ONE 12(2): e0172073. doi:10.1371/journal.pone.0172073
Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of interorganizational networks led to the increase of Japanese automakers’ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.
Analyzing the coevolution of interorganizational networks and organizational performance: Automakers’ production networks in Japan
Matous, P. & Todo, Y. Appl Netw Sci (2017) 2: 5. doi:10.1007/s41109-017-0024-5
I am a big fan of A16Z podcasts, and they just released a terrific episode on Network Effect. Network effect is an important, and somewhat confusing topic. The reason network effect is important is that the businesses with a true network effect are highly defensible, have strong retention and engagement, exhibit characteristics of a monopoly, and tend to last for…
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