|
***** To join INSNA, visit http://www.insna.org *****
Barry Wellman
A vision is just a vision if it's only in your head
Step by step, link by link, putting it together
Streisand/Sondheim
_______________________________________________________________________
NetLab Network FRSC INSNA Founder
http://www.chass.utoronto.ca/~wellman twitter: @barrywellman
NETWORKED:The New Social Operating System Lee Rainie & Barry Wellman
MIT Press http://amzn.to/zXZg39 Print $17 Kindle $11
_______________________________________________________________________
Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d4f939f1f8&e=55e25a0e3e
Dynamical Systems on Networks
When studying a dynamical process, one is concerned with its behavior as a function of time, space, and its parameters. There are numerous studies that examine how many people are infected by a biological contagion and whether it persists from one season to another, whether and to what extent interacting oscillators synchronize, whether a meme on the internet becomes viral or not, and more. These studies all have something in common: the dynamics are occurring on a set of discrete entities (the nodes in a network) that are connected to each other via edges in some nontrivial way. This leads to the natural question of how such underlying nontrivial connectivity affects dynamical processes. This is one of the most important questions in network science, and it is the core question that we consider in our tutorial.
Dynamical Systems on Networks
A Tutorial
Authors: Mason A. Porter, James P. Gleeson
ISBN: 978-3-319-26640-4 (Print) 978-3-319-26641-1
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7a96edce58&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=df46cf0258&e=55e25a0e3e) , via CxBooks (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=26bb37a461&e=55e25a0e3e)
Is this scaling nonlinear?
One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g., patents) scale nonlinearly with the population~x of the cities in which they appear, i.e., y˙˙x^˙˙, ˙˙˙˙1. More recently, the generality of this finding has been questioned in studies using new databases and different definitions of city boundaries. In this paper we investigate the existence of nonlinear scaling using a probabilistic framework in which fluctuations are accounted explicitly. In particular, we show that this allows not only to (a) estimate ˙˙ and confidence intervals, but also to (b) quantify the evidence in favor of ˙˙˙˙1 and (c) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare 5 different models to 15 different datasets and we find that the answers to points (a)-(c) crucially depend on the fluctuations contained in the data, on how they are modeled, and on the fact that the city sizes are
heavy-tailed distributed.
Is this scaling nonlinear?
J. C. Leitao, J.M. Miotto, M. Gerlach, E. G. Altmann
http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=307593fe70&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=97b0975b37&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ccfd70d573&e=55e25a0e3e)
Topical issue: Temporal Network Theory and Applications Eur. Phys. J. B
The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.
Modern temporal network theory: a colloquium*
Petter Holme
Topical issue: Temporal Network Theory and Applications
Eur. Phys. J. B (2015) 88: 234
http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=66095b2063&e=55e25a0e3e
Complexity Digest's insight:
See Topical Issue: http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=55dc36af54&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=36c7cd662f&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4a429beb2a&e=55e25a0e3e)
The physics of multilayer networks
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object.
On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.
The physics of multilayer networks
Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas
http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e1643cf37c&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9da06f25c8&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e498d17a62&e=55e25a0e3e)
Linking Individual and Collective Behavior in Adaptive Social Networks
Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N-person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the
evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
Linking Individual and Collective Behavior in Adaptive Social Networks
Flávio L. Pinheiro, Francisco C. Santos, and Jorge M. Pacheco
Phys. Rev. Lett. 116, 128702
http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e70b677290&e=55e25a0e3e
Complexity Digest's insight:
If networks adapt faster, then less cooperators are required for cooperation to prevail.
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ecd4f73e8c&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c0197cb049&e=55e25a0e3e)
Calling Dunbar's Numbers
The social brain hypothesis predicts that humans have an average of
about 150 relationships at any given time. Within this 150, there are
layers of friends of an ego, where the number of friends in a layer
increases as the emotional closeness decreases. Here we analyse a mobile
phone dataset, firstly, to ascertain whether layers of friends can be
identified based on call frequency. We then apply different clustering
algorithms to break the call frequency of egos into clusters and compare
the number of alters in each cluster with the layer size predicted by the
social brain hypothesis. In this dataset we find strong evidence for the
existence of a layered structure. The clustering yields results that match
well with previous studies for the innermost and outermost layers, but for
layers in between we observe large variability.
Calling Dunbar's Numbers
Pádraig MacCarron, Kimmo Kaski, Robin Dunbar
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=04e438bc8a&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=60744f7848&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a3c79bef5d&e=55e25a0e3e)
Predation risk drives social complexity in cooperative breeders
It is widely accepted that high predation risk may select for group living, but predation is not regarded as a primary driver of social complexity. This view neglects the important effect of predation on dispersal and offspring survival, which may require cooperation among group members. The significance of predation for the evolution of social complexity can be well illustrated by behavioral and morphological adaptations of highly social animals showing division of labor, such as eusocial insects and cooperatively breeding fishes. By examining the diversity of social organization in a cooperative cichlid in relation to ecological variation, we show that predation risk has the greatest explanatory power of social complexity. This stresses the significance of predation for social evolution.
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8135dd138b&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c0822a5573&e=55e25a0e3e)
==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7390b4c6fd&e=55e25a0e3e ) and using the "Suggest" button.
==============================================
==============================================
_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.
|
