📄Social network analysis for social neuroscientists
📘Journal: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
(I.F= 4.235)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #neuroscientists
📘Journal: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
(I.F= 4.235)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #neuroscientists
📄Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Signal_Processing #Machine_Learning
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Signal_Processing #Machine_Learning
📄How to get started with Graph Machine Learning
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
Medium
How to get started with Graph Machine Learning
Deep learning update: What have I learned about Graph ML in 2 months?
2020_Linking_Network_Characteristics_of_Online_Social_Networks_to.pdf
613.7 KB
📄Linking Network Characteristics of Online Social Networks to Individual Health: A Systematic Review of Literature
📘Journal: Health Communication (I.F= 3.501)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Health #review
📘Journal: Health Communication (I.F= 3.501)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Health #review
👍1
🎞 Graph-Powered Machine Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
YouTube
Graph-Powered Machine Learning
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds…
📄Applications of Graph Neural Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Neural_Networks #GNN
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Neural_Networks #GNN
Medium
Applications of Graph Neural Networks
Exploring the forays of GNN based techniques into diverse domains
📄Survey on graph embeddings and their applications to machine learning problems on graphs
📘Journal: PeerJ Computer Science (I.F= 2.41)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #graph_embedding #Machine_Learning
📘Journal: PeerJ Computer Science (I.F= 2.41)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #graph_embedding #Machine_Learning
2018_Opinion leader detection A methodological review.pdf
7.7 MB
📄Opinion leader detection: A methodological review
📘Journal: EXPERT SYSTEMS WITH APPLICATIONS (I.F=8.665)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #leader #review
📘Journal: EXPERT SYSTEMS WITH APPLICATIONS (I.F=8.665)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #leader #review
📄Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Acceleration #Survey
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Acceleration #Survey
📄Representation Learning on Graphs: Methods and Applications
📘Journal: IEEE Data Engineering Bulletin
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Representation_Learning
📘Journal: IEEE Data Engineering Bulletin
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Representation_Learning
🎞 Graph Search, Shortest Paths, and Data Structures
💥Free recorded course by Tim Roughgarden
💥The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
💥Free recorded course by Tim Roughgarden
💥The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
Coursera
Graph Search, Shortest Paths, and Data Structures
Offered by Stanford University. The primary topics in ... Enroll for free.
📘 Graph Representation Learning
💥Free online book by William L. Hamilton
🗓Publish year: 2020
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph
💥Free online book by William L. Hamilton
🗓Publish year: 2020
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph
📄Machine learning on Graphs course: Pre-requisites
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graph #TensorFlow
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graph #TensorFlow
Medium
Machine learning on Graphs course: Pre-requisites
(This is part of a four part course hosted by Octavian.ai this summer)
📄Deep Graph Learning: Foundations, Advances and Applications
📘Conference: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📘Conference: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📘 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
👍1
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.