A detailed characterization of complex networks using Information Theory
“two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyze networks”
https://t.co/xc8ZxboAuc
“two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyze networks”
https://t.co/xc8ZxboAuc
Machine learning dynamical phase transitions in complex networks
Qi Ni, Ming Tang, Ying Liu, Ying-Cheng Lai
https://arxiv.org/abs/1911.04633
In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.
https://arxiv.org/abs/1911.04633
Qi Ni, Ming Tang, Ying Liu, Ying-Cheng Lai
https://arxiv.org/abs/1911.04633
In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.
https://arxiv.org/abs/1911.04633
سلام
ممنون میشویم اگر در کامل کردن این پرسشنامه پژوهشی مشارکت کنید:
https://docs.google.com/forms/d/e/1FAIpQLSf66T23rTJohHJ8_EgEjAC7GxEpdk9cw5IL2-yCUuwcrti9QA/viewform
ممنون میشویم اگر در کامل کردن این پرسشنامه پژوهشی مشارکت کنید:
https://docs.google.com/forms/d/e/1FAIpQLSf66T23rTJohHJ8_EgEjAC7GxEpdk9cw5IL2-yCUuwcrti9QA/viewform
Google Docs
پرسشنامه واکنش پذیری بین فردی
لطفا مشخصات فردی خود را وارد کنید.
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
عنوان:
Twitter Application Programming Interface
🗣 پرهام مرادی - دانشگاه شهید بهشتی
⏰ دوشنبه، 27 آبان - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
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🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
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عنوان:
Twitter Application Programming Interface
🗣 پرهام مرادی - دانشگاه شهید بهشتی
⏰ دوشنبه، 27 آبان - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
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💰 Fully funded #PhD position for research into social physics, complex systems, and innovation at University of Ghent (Belgium)
https://t.co/8zTAz5Zlll
https://t.co/8zTAz5Zlll
www.ugent.be
PhD Student — Ghent University
oap-en - 100% - 2020/01/14 00:00:00 GMT+1 - EB21
"Machine Learning: Mathematical Theory and Scientific Applications"
by Weinan E
https://t.co/dj6433mGVS
by Weinan E
https://t.co/dj6433mGVS
💰 #Master in Physics of Complex Systems IFISC: photonics, statistical physics, biosystems, sociology, quantum devices https://t.co/F07D0p1r0k
YouTube
Master's Degree in Physics of Complex Systems
The Master in Physics of Complex Systems is an official degree offered by the University of the Balearic Islands (UIB) in collaboration with the Spanish Nati...
PHYSICS OF COMPLEX SYSTEMS
LECTURE NOTES
SEPTEMBER 6, 2019;
PROF. DR. HAYEHINRICHSE
http://teaching.hayehinrichsen.de/lecturenotes/cs.pdf
LECTURE NOTES
SEPTEMBER 6, 2019;
PROF. DR. HAYEHINRICHSE
http://teaching.hayehinrichsen.de/lecturenotes/cs.pdf
Another #PhD position opened up in Amsterdam. This time a more applied position on network analysis and clinical interventions. I wrote a blog on this and all other job opportunities (1 ass. prof & 3 PhDs) related to our group at https://t.co/jR3eejIhfy Please share!
The website of my Phd course on "Maximum Entropy Ensemble of Networks " is now live! (now 3/5 lesson are posted) Feel free to give a look!
https://t.co/LSaib2OZEr
https://t.co/LSaib2OZEr
"Chi-Squared Data Analysis and Model Testing for Beginners" - new book from Carey Witkov and Keith Zengel, Oxford, 2019: https://t.co/81FpnDleRt