📘 Network Science
💥Free online book by Albert-László Barabási
💥The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2015. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#online_book
💥Free online book by Albert-László Barabási
💥The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2015. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#online_book
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2019_Survey_on_Opinion_Leader_in_Social_Network_using_Data_Mining.pdf
434.7 KB
📄Survey on Opinion Leader in Social Network using Data Mining
📘Conference: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Data_Mining
📘Conference: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Data_Mining
🎞 Machine learning on graphs
💥Free recorded course by Alexander S. Kulikov
💥The course has a couple of components:
▪️Projects - Google Colab documents that guide you through writing python and TensorFlow code to solve problems.
▪️Project solutions - A week after a project is published, the solution will be published. It'll be linked to from the original project so as not to spoil the project for new visitors.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_learning #code #python
💥Free recorded course by Alexander S. Kulikov
💥The course has a couple of components:
▪️Projects - Google Colab documents that guide you through writing python and TensorFlow code to solve problems.
▪️Project solutions - A week after a project is published, the solution will be published. It'll be linked to from the original project so as not to spoil the project for new visitors.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_learning #code #python
📄Utilizing graph machine learning within drug discovery and development
📘Journal: Briefings in Bioinformatics(I.F=11.622)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_learning
📘Journal: Briefings in Bioinformatics(I.F=11.622)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_learning
📘 Deep Learning on Graphs
💥Free online book by Yao Ma and Jiliang Tang
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph #Deep_Learning
💥Free online book by Yao Ma and Jiliang Tang
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph #Deep_Learning
🎞Trees and Graphs: Basics
💥Free recorded course by Sriram Sankaranarayanan
💥Basic algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures and basic traversal algorithms on graphs. This course also covers advanced topics such as kd-trees for spatial data and algorithms for spatial data.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
💥Free recorded course by Sriram Sankaranarayanan
💥Basic algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures and basic traversal algorithms on graphs. This course also covers advanced topics such as kd-trees for spatial data and algorithms for spatial data.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
Coursera
Trees and Graphs: Basics
Offered by University of Colorado Boulder. Basic ... Enroll for free.
2018_A_Systematic_Survey_of_Opinion_Leader_in_Online_Social_Network.pdf
216 KB
📄A Systematic Survey of Opinion Leader in Online Social Network
📘Conference: 2018 International Conference on Soft-computing and Network Security (ICSNS)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey
📘Conference: 2018 International Conference on Soft-computing and Network Security (ICSNS)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey
🎞 Introduction to Graph Theory
💥Free recorded course by Alexander S. Kulikov
💥In this online course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible!
By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
💥Free recorded course by Alexander S. Kulikov
💥In this online course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible!
By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
Coursera
Introduction to Graph Theory
Offered by University of California San Diego. We invite ... Enroll for free.
📄Nature‑inspired optimization algorithms for community detection in complex networks: a review and future trends
📘Journal: Telecommunication Systems(I.F=2.336)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #optimization_algorithms #community #trends #review
📘Journal: Telecommunication Systems(I.F=2.336)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #optimization_algorithms #community #trends #review
🎞 Machine learning and link prediction
💥Free recorded tutorial by Mark Needham & Jennifer Reif
💥In this session, will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_learning
💥Free recorded tutorial by Mark Needham & Jennifer Reif
💥In this session, will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_learning
YouTube
Machine learning and link prediction by Mark Needham & Jennifer Reif
Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack the…
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2021_New_research_methods_&_algorithms_in_social_network_analysis.pdf
525.4 KB
📄New research methods & algorithms in social network analysis
📘Journal: Future Generation Computer Systems (I.F=8.872 )
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
📘Journal: Future Generation Computer Systems (I.F=8.872 )
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
2020-Finding key players in complex networks through.pdf
2.4 MB
📄Finding key players in complex networks through deep reinforcement learning
📘Journal: Nature Machine Intelligence (I.F=25.9)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #deep_reinforcement_learning
📘Journal: Nature Machine Intelligence (I.F=25.9)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #deep_reinforcement_learning
📄deep learning for Complex Networks
💥research paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #deep_Learning
💥research paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #deep_Learning
TU Delft
Deep learning for complex networks
📄Complex Networks and Machine Learning: From Molecular to Social Sciences
📘Journal: applied science (I.F=2.679)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
📘Journal: applied science (I.F=2.679)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
2015_Estimating_Complex_Networks_Centrality_via_neural_networks.pdf
1 MB
📄Estimating Complex Networks Centrality via neural networks and machine learning
📘Conference : 2015 International Joint Conference on Neural Networks (IJCNN)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
📘Conference : 2015 International Joint Conference on Neural Networks (IJCNN)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
🎞 Lecture12. Link Prediction
💥Free recorded Lecture on Link Prediction
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Prediction
💥Free recorded Lecture on Link Prediction
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Prediction
YouTube
Lecture12. Link Prediction
Network Science 2021 @ HSE
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📄A survey of data mining and social network analysis based anomaly detection techniques
📘Journal: EGYPTIAN INFORMATICS JOURNAL (I.F= 4.195)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #data_mining #anomaly_detection #survey
📘Journal: EGYPTIAN INFORMATICS JOURNAL (I.F= 4.195)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #data_mining #anomaly_detection #survey
🎞 Course "Social Network Analysis". Lecture 1. Terminology
💥Free recorded course by Leonid Zhukov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Terminology
💥Free recorded course by Leonid Zhukov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Terminology
YouTube
Course "Social Network Analysis" (Leonid Zhukov). Lecture 1. Terminology
Course outline:
- Introduction to network science
- Denoscriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
- Introduction to network science
- Denoscriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
2016-Machine Learning in Complex Networks (1).pdf
8.5 MB
📘 Machine Learning in Complex Networks
📝Authors: Thiago Christiano Silva, Liang Zhao
📅Publish year: 2016
💥This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Machine_Learning
📝Authors: Thiago Christiano Silva, Liang Zhao
📅Publish year: 2016
💥This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Machine_Learning
👍3
📄A survey on text mining in social networks
📘Journal: KNOWLEDGE ENGINEERING REVIEW (I.F= 2.016)
🗓Publish year: 2015
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #text_mining #survey
📘Journal: KNOWLEDGE ENGINEERING REVIEW (I.F= 2.016)
🗓Publish year: 2015
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #text_mining #survey