🎞 We are biased but the Internet could help us measure it & possibly fix it. Also, let's save the Internet instead of blaming it for everything!
https://t.co/eN9hRTrrhZ
https://t.co/eN9hRTrrhZ
YouTube
The Internet and your inner English tea merchant | Taha Yasseri | TEDxThessaloniki
The Internet is a totally internet phenomenon. In this talk, Dr Taha Yasseri gives answers to burning internet questions. Are users biased like an English te...
Calling all quantitative life scientists! Deadline to apply to this postdoc is July 10, 2019!
https://t.co/1utmJVxueR
https://t.co/1utmJVxueR
🎞 Taha Yasseri course on "Research Design in Social Data Science" is finally live!
Here is a sneak preview!
If you like it, enrol to join the next cohort, starting 10 Oct, here:
https://t.co/xkiREH7yk3
Here is a sneak preview!
If you like it, enrol to join the next cohort, starting 10 Oct, here:
https://t.co/xkiREH7yk3
SAGE Campus - Online Data Science Courses for Social Scientists from Sage Publishing
Research Design in Social Data Science — SAGE Campus - Online Data Science Courses for Social Scientists from Sage Publishing
SAGE Campus online data science course on research design in social data science. This short course is perfect for social scientists looking to work with big data. Enroll or register your interest today for just $199.
terrific paper on causal emergence: https://t.co/ZNBVvq0dhy
Since then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it https://t.co/Til7g7LCEk
Since then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it https://t.co/Til7g7LCEk
Complex Systems Studies
terrific paper on causal emergence: https://t.co/ZNBVvq0dhy Since then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it https://t.co/Til7g7LCEk
Networks are such powerful objects. They've changed how we study complex systems. But I’ve always been struck by how nontrivial the “what is a node?” question can be.
We provide a framework for identifying the most informative *scale* to describe interdependencies in a system...
We provide a framework for identifying the most informative *scale* to describe interdependencies in a system...
Complex Systems Studies
Networks are such powerful objects. They've changed how we study complex systems. But I’ve always been struck by how nontrivial the “what is a node?” question can be. We provide a framework for identifying the most informative *scale* to describe interdependencies…
...which is to say, we find that compressed or coarse-grained or macroscale denoscriptions of networks often have more *effective information* than the original microscale network (e.g. your raw network data).
This noise-minimizing process is known as causal emergence.
So what?
This noise-minimizing process is known as causal emergence.
So what?
Complex Systems Studies
...which is to say, we find that compressed or coarse-grained or macroscale denoscriptions of networks often have more *effective information* than the original microscale network (e.g. your raw network data). This noise-minimizing process is known as causal…
It's a question of zoom: what's the right scale to represent brain networks, given what we want from our model? What's the best scale to model economic systems? What counts as a "node" in a genome?
They're rich, tough, fun problems. And there's a long way to go.
They're rich, tough, fun problems. And there's a long way to go.
Complex Systems Studies
It's a question of zoom: what's the right scale to represent brain networks, given what we want from our model? What's the best scale to model economic systems? What counts as a "node" in a genome? They're rich, tough, fun problems. And there's a long way…
This research fascinates me, and there's a bunch of directions to go with it.
Feel free to send feedback or questions, and stay tuned for the release of some tutorials / open python code.
https://t.co/Til7g7LCEk
Feel free to send feedback or questions, and stay tuned for the release of some tutorials / open python code.
https://t.co/Til7g7LCEk
arXiv.org
Uncertainty and causal emergence in complex networks
The connectivity of a network conveys information about the dependencies
between nodes. We show that this information can be analyzed by measuring the
uncertainty (and certainty) contained in...
between nodes. We show that this information can be analyzed by measuring the
uncertainty (and certainty) contained in...
What are the gender differences in scientific productivity and impact?
We reconstructed 1.5 million careers with fascinating findings at
https://t.co/5VuiWpxXxo.
1. Paradoxically, as the fraction of women has increased in academia, so did the productivity and impact gender gaps.
2. The good news: there are no systematic differences between the annual productivity of male and female scientists! Annual productivity is a key gender-invariant.
3. There is, however, a persistent higher dropout rate for women at all stages of their careers.
4. Once we correct for the different dropout rate, the productivity and the impact differences between female and male scientists are reduced by roughly two-thirds.
5. We also discover a second gender invariant quantity: men and women have equivalent career-wise impact for the same size body of work (total number of publications).
The bottom line:
https://twitter.com/barabasi/status/1149210323724984321?s=19
We reconstructed 1.5 million careers with fascinating findings at
https://t.co/5VuiWpxXxo.
1. Paradoxically, as the fraction of women has increased in academia, so did the productivity and impact gender gaps.
2. The good news: there are no systematic differences between the annual productivity of male and female scientists! Annual productivity is a key gender-invariant.
3. There is, however, a persistent higher dropout rate for women at all stages of their careers.
4. Once we correct for the different dropout rate, the productivity and the impact differences between female and male scientists are reduced by roughly two-thirds.
5. We also discover a second gender invariant quantity: men and women have equivalent career-wise impact for the same size body of work (total number of publications).
The bottom line:
https://twitter.com/barabasi/status/1149210323724984321?s=19
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
«مقدمهای بر حسابان کسری و کاربردهای آن»
🗣 معین خلیقی - دانشگاه تربیت مدرس
⏰ دوشنبه، ۲۴ تیر - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
⭕️ به امید دیدار
📞 @herman1
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
«مقدمهای بر حسابان کسری و کاربردهای آن»
🗣 معین خلیقی - دانشگاه تربیت مدرس
⏰ دوشنبه، ۲۴ تیر - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
⭕️ به امید دیدار
📞 @herman1
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
⌨ In July’s issue of Nature Machine Intelligence, find out about machine intelligence in drug discovery, an approach for automatic classification of answers in free-form surveys and whether AI will soon surpass humans in playing Angry Birds!
https://go.nature.com/2S4N24P
https://go.nature.com/2S4N24P
💡 Statistical mechanics of time series.
https://arxiv.org/abs/1907.04925
Countless natural and social multivariate systems are studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications on a set of stock market returns from the NYSE.
https://arxiv.org/abs/1907.04925
Countless natural and social multivariate systems are studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications on a set of stock market returns from the NYSE.
The State of the Art in Multilayer Network Visualization
“literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks, and analytic techniques”
https://t.co/o6sRQfXIJY
“literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks, and analytic techniques”
https://t.co/o6sRQfXIJY
💡 Network science has found many applications in natural language processing (NLP) and text mining. In this recent work, we present a brief introduction to text networks, from their respective construction to applications:
https://t.co/aX34NOhan8
https://t.co/aX34NOhan8
ResearchGate
(PDF) Text Networks (CDT-4)
PDF | On Sep 2, 2018, Henrique Ferraz de Arruda and others published Text Networks (CDT-4)
💡 لیست اساتیدی که در ایران به روی سیستمهای پیچیده کار میکنند:
https://ccnsd.ir/people/icss/
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این لیست در حال کامل شدن است.
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https://ccnsd.ir/people/icss/
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این لیست در حال کامل شدن است.
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Lecture Notes in Social Networks comprises volumes covering the theory, foundations and applications of the new emerging multidisciplinary field of social networks analysis and mining.
See more…
https://t.co/I95yzngObH
See more…
https://t.co/I95yzngObH
Micro, Meso, Macro: the effect of triangles on communities in networks
“communities can emerge spontaneously from simple processes of motiff generation happening at a micro-level”
https://t.co/plew3LOnrg
“communities can emerge spontaneously from simple processes of motiff generation happening at a micro-level”
https://t.co/plew3LOnrg