Complex Systems Studies
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@ComplexSys

#complexity #complex_systems #networks #network_science

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📃Sensitivity of Complex Networks

https://arxiv.org/abs/1610.05264

Marco Tulio Angulo, Gabor Lippner, Yang-Yu Liu, Albert-László #Barabási

📌 ABSTRACT:
The sensitivity (i.e. dynamic response) of complex networked systems has not been well understood, making difficult to predict whether new macroscopic dynamic behavior will emerge even if we know exactly how individual nodes behave and how they are coupled. Here we build a framework to quantify the sensitivity of complex networked system of coupled dynamic units. We characterize necessary and sufficient conditions for the emergence of new macroscopic dynamic behavior in the thermodynamic limit. We prove that these conditions are satisfied only for architectures with power-law degree distributions. Surprisingly, we find that highly connected nodes (i.e. hubs) only dominate the sensitivity of the network up to certain critical frequency.
انجمن علمی فیزیک بهشتی (SBU)
سمینار عمومی هفتگی، سه شنبه ساعت 16 سالن ابن هیثم دانشکده فیزیک @sbu_physics
💡چگونه در مورد پایداری یک شبکه اجتماعی حرف می‌زنیم؟
💡چگونه در مورد جنگ سوریه/عراق اظهار نظر می‌کنیم؟!
💡آیا مدل‌سازی ریاضی وجود دارد که نتیجه جنگ جهانی را بتواند پیش‌بینی کند؟

نگاه کنید به این نوشته:

🔵 بالانس تئوری چی میگه؟!
http://www.sitpor.org/2015/08/balance-theory/
Complex Systems Studies
http://firstmonday.org/ojs/index.php/fm/article/view/941/863
📌 Abstract
This paper looks at mapping covert networks using data available from news sources on the World Wide Web. Specifically, we examine the network surrounding the tragic events of September 11th 2001. Through public data we are able to map a portion of the network centered on the 19 dead hijackers. This map gives us some insight into the terrorist organization, yet it is incomplete. Suggestions for further work and research are offered.
Dear SFI Community,

We are pleased to announce our 2017 Complex Systems Summer School. But we need your help. If you know graduate students and postdocs who might be interested in becoming a Complexity Scholar, please forward this email to them. Below is some information about the 2017 CSSS, including the fast-approaching January 23 application deadline. Thank you for being an ambassador of SFI and complex systems science.

The Complex Systems Summer School offers an intensive four week introduction to complex behavior in mathematical, physical, living, and social systems for graduate students, postdoctoral fellows, and professionals in the sciences and social sciences. The school is for participants who seek background and hands-on experience to help them prepare to conduct interdisciplinary research in areas related to complex systems.

Application deadline: January 23, 2017

Who is eligible: Graduate students and postdoctoral fellows in any discipline. Proficiency in English is required.

APPLY NOW HERE
https://sficsss.fluidreview.com/

Further information about the program can be found on our website.
http://tuvalu.santafe.edu/events/workshops/index.php/Complex_Systems_Summer_School_2017_(CSSS)
📃 Group Minds and the Case of Wikipedia
Simon DeDeo

https://arxiv.org/abs/1407.2210

📌 ABSTRACT:
Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors, showing how some, but not all, effects of individual experience persist in the aggregate. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions, and attributions of cognitive states of the form "what the group believes" and "what the group values".
📃 Phase Transitions in Community Detection and Clustering
Cristopher Moore, Santa Fe Institute

tuvalu.santafe.edu/events/workshops/images/b/b4/Csss16-networks.pdf
💡 Information and links to courses related to Complex Systems:

https://www.complexityexplorer.org/explore/syllabi
#سلسله_سمینارهای_هفتگی گروه سیستم های پیچیده شهید بهشتی
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
🔵 Complex systems Winter School 2017:
Early Registration Deadline on Nov. 2

http://necsi.edu/education/school.html
📝 Social Network Analysis Reveals Potential Fission-Fusion Behavior in a Shark

Danielle E. Haulsee, Dewayne A. Fox[…]Matthew J. Oliver

📌Abstract
Complex social networks and behaviors are difficult to observe for free-living marine species, especially those that move great distances. Using implanted acoustic transceivers to study the inter- and intraspecific interactions of sand tiger sharks Carcharias taurus, we observed group behavior that has historically been associated with higher order mammals. We found evidence strongly suggestive of fission-fusion behavior, or changes in group size and composition of sand tigers, related to five behavioral modes (summering, south migration, community bottleneck, dispersal, north migration). Our study shows sexually dimorphic behavior during migration, in addition to presenting evidence of a potential solitary phase for these typically gregarious sharks. Sand tigers spent up to 95 consecutive and 335 cumulative hours together, with the strongest relationships occurring between males. Species that exhibit fission-fusion group dynamics pose a particularly challenging issue for conservation and management because changes in group size and composition affect population estimates and amplify anthropogenic impacts.

http://www.nature.com/articles/srep34087
📝 Encoding Temporal Markov Dynamics in Graph for Time Series Visualization

https://arxiv.org/abs/1610.07273

📌ABSTRACT:
Time series is attracting more attention across statistics, machine learning and pattern recognition as it appears widely in both industry and academia, but few advances has been achieved in effective time series visualization due to its temporal dimensionality and complex dynamics. Inspired by recent effort on using network metrics to characterize time series for classification, we present an approach to visualize time series as complex networks based on first order Markov process and temporal ordering. Different to classical bar charts, line plots and other statistics based graph, our approach delivers more intuitive visualization that better preserves both the temporal dependency and frequency structures. It provides a natural inverse operation to map the graph back to time series, making it possible to use graph statistics to characterize time series for better visual exploration and statistical analysis. Our experimental results suggest the effectiveness on various tasks such as system identification, classification and anomaly detection on both synthetic and the real time series data.
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