๐ Machine Learning with Graphs: Deep Generative Models for Graphs, Graph RNN: Generating Realistic Graphs, Scaling Up & Evaluating Graph Gen, Applications of Deep Graph Generation.
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฅthis lecture, focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in next parts introduce and focus on GraphRNN, one of the first deep generative models for graph; and finally, discuss GCPN, a deep graph generative model designed specifically for application to molecule generation.
๐ฝ Watch: part1 part2 part3 part4
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฅthis lecture, focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in next parts introduce and focus on GraphRNN, one of the first deep generative models for graph; and finally, discuss GCPN, a deep graph generative model designed specifically for application to molecule generation.
๐ฝ Watch: part1 part2 part3 part4
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.1 - Deep Generative Models for Graphs
For more information about Stanfordโs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Ex8TsH
Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types ofโฆ
Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types ofโฆ
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๐ application of machine learning in traffic optimization
๐ฅFree recorded course by Powel gora
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
๐ฅFree recorded course by Powel gora
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Paweล Gora: Applications of machine learning in traffic optimization
I will be talking about possible applications of machine learning in traffic optimization (and in optimizing some other complex processes). I will describe the process of building traffic metamodels by approximating outcomes of traffic simulations using machineโฆ
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๐ Machine Learning with Graphs: Applications of Deep Graph Generation.
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanfordโs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyโฆ
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyโฆ
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๐Graph Attention Networks
๐ฅTechnical paper
๐ Study
๐ฒChannel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
๐ฅTechnical paper
๐ Study
๐ฒChannel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
Baeldung on Computer Science
Graph Attention Networks | Baeldung on Computer Science
Explore graph neural networks that use attention.
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๐ Machine Learning with Graphs: Generative Models for Graphs
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฅIn this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Generative_Models
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฅIn this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Generative_Models
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs
For more information about Stanfordโs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jO8OsE
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generativeโฆ
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generativeโฆ
๐6
๐ Graph Analytics and Graph-based Machine Learning
๐ฅFree recorded course by Clair Sullivan(Neo4j)
๐ฅMachine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
๐ฅFree recorded course by Clair Sullivan(Neo4j)
๐ฅMachine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Graph Analytics and Graph-based Machine Learning
Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between dataโฆ
๐4๐1
๐Machine Learning for Refining Knowledge Graphs: A Survey
๐ Journal: acm digital library (I.F=14.324)
๐Publish year: 2020
๐Study paper
๐ฒChannel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
๐ Journal: acm digital library (I.F=14.324)
๐Publish year: 2020
๐Study paper
๐ฒChannel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
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๐น Graph Embedding For Machine Learning in Python
๐ฅIn this video, you will learn how to embed graphs into n-dimensional space to use them for machine learning.
๐ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Graph_Embedding #Machine_Learning
๐ฅIn this video, you will learn how to embed graphs into n-dimensional space to use them for machine learning.
๐ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Graph_Embedding #Machine_Learning
YouTube
Graph Embedding For Machine Learning in Python
In this video, we learn how to embed graphs into n-dimensional space to use them for machine learning.
DeepWalk Paper: https://arxiv.org/abs/1403.6652
โพโพโพโพโพโพโพโพโพโพโพโพโพโพโพโพโพ
๐ Programming Books & Merch ๐
๐ The Python Bible Book: https://www.neuralnine.com/books/โฆ
DeepWalk Paper: https://arxiv.org/abs/1403.6652
โพโพโพโพโพโพโพโพโพโพโพโพโพโพโพโพโพ
๐ Programming Books & Merch ๐
๐ The Python Bible Book: https://www.neuralnine.com/books/โฆ
๐5
๐ Machine Learning with Graphs: Applications of Deep Graph Generation.
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
๐ฅFree recorded course by Jure Leskovec, Computer Science, PhD
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanfordโs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyโฆ
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyโฆ
๐1
๐ Machine Learning with Graphs - Node Embeddings
๐ฅSDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
๐ฅSDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
YouTube
Machine Learning with Graphs - Node Embeddings
SDML is partnering with Houston Machine Learning on a series about machine learning with graphs. The content will be mainly based on the Stanford course: https://web.stanford.edu/class/cs224w/
Series schedule:
Introduction; Machine Learning for Graphs
Traditionalโฆ
Series schedule:
Introduction; Machine Learning for Graphs
Traditionalโฆ
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