π Machine Learning with Graphs: Theory of Graph Neural Networks
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
π½ Watch: part1 part2 part3
πSlides
π»code
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
π½ Watch: part1 part2 part3
πSlides
π»code
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.1 - A general Perspective on GNNs
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BjIqNd
Lecture 7.1 - A General Perspective on Graph Neural Networks
Jure Leskovec
Computer Science, PhD
In this lecture, we introduceβ¦
Lecture 7.1 - A General Perspective on Graph Neural Networks
Jure Leskovec
Computer Science, PhD
In this lecture, we introduceβ¦
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2017-Python for Graph and Network Analysis.pdf
13 MB
πPython for Graph and Network Analysis
πPublish year: 2017
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book #Python #Graph
πPublish year: 2017
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book #Python #Graph
π3
πPython modularity Examples
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
πCommunity Detection
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
π1
πGCN-tutorial
π₯Technical paper
π₯ Graph Convolutional Network. Perform convolution operations on a graph using the information embedded into each node. The main idea is to "look" at neighboor nodes and update the currently embedded information into a higher or lower dimensional space by performing a ReLU or softmax operation.
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GCN #Coda
π₯Technical paper
π₯ Graph Convolutional Network. Perform convolution operations on a graph using the information embedded into each node. The main idea is to "look" at neighboor nodes and update the currently embedded information into a higher or lower dimensional space by performing a ReLU or softmax operation.
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GCN #Coda
π3
π pytorch geometric tutorial: graph attention networks implementation
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GAT #code #python
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GAT #code #python
YouTube
Pytorch Geometric tutorial: Graph attention networks (GAT) implementation
In this video we will see the math behind GAT and a simple implementation in Pytorch geometric.
Outcome:
- Recap
- Introduction
- GAT
- Message Passing pytroch layer
- Simple GCNlayer implementation
- GAT implementation
- GAT Usage
Download the materialβ¦
Outcome:
- Recap
- Introduction
- GAT
- Message Passing pytroch layer
- Simple GCNlayer implementation
- GAT implementation
- GAT Usage
Download the materialβ¦
π2
πGraph Attention Networks Paper Explained With Illustration and PyTorch Implementation
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GAT #Coda
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GAT #Coda
towardsai.net
Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation | Towards AI
Author(s): Ebrahim Pichka Originally published on Towards AI. A detailed and illustrated walkthrough of the βGraph Attention Networksβ paper by VeliΔkoviΔ e ...
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πTutorial: Graph Neural Networks in TensorFlow: A Practical Guide
π₯Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
π₯Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
π₯Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
π₯Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
YouTube
Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
Organizers: Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neuralβ¦
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neuralβ¦
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