📄Graph Neural Networks in IoT: A Survey
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #IOT #survey
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #IOT #survey
👍2
📄Statistical Network Analysis: Past, Present, and Future
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Statistical_Network #Past #Present #Future
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Statistical_Network #Past #Present #Future
❤1👍1
🎞 Social Network Analysis | Chapter 4 | Link Analysis | Part 2
💥This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Analysis
💥This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Analysis
YouTube
Social Network Analysis | Chapter 4 | Link Analysis | Part 2
This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
❤1
📄A Gentle Introduction to Graph Neural Networks
💥Technical online article
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #GNN
💥Technical online article
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #GNN
Distill
A Gentle Introduction to Graph Neural Networks
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
👍5
🎞 Machine Learning with Graphs: Generative Models for Graphs, Erdos Renyi Random Graphs, The Small World Model, Kronecker Graph Model
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥This lecture, 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. The simplest model for graph generation, Erdös-Renyi graph (E-R graphs, Gnp graphs). The small-world graphs (Watts–Strogatz graphs, W-S graphs). Even though the E-R graphs can fit the average path length of real-world graphs, its clustering coefficient is much smaller than real-world graphs. The small-world model is proposed to generative realistic graphs with both low diameter and high clustering coefficient. Specifically, W-S graphs are generative by randomly rewring edges from regular lattic graphs. The Kronecker Graph model, where graphs are generated in a recursive manner. The key motivation is that real-world graphs often exhibit self-similarity, where the whole structure of the graph has the same shape as its parts. Kronecker graphs are generated by recursively doing Kronecker product over the initiator matrix, which is trained to fit the statistics of the input dataset. We further discuss fast Kronecker generator algorithms. Finally, we show that Kronecker graphs and real graphs are very close in many important graph statistics.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥This lecture, 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. The simplest model for graph generation, Erdös-Renyi graph (E-R graphs, Gnp graphs). The small-world graphs (Watts–Strogatz graphs, W-S graphs). Even though the E-R graphs can fit the average path length of real-world graphs, its clustering coefficient is much smaller than real-world graphs. The small-world model is proposed to generative realistic graphs with both low diameter and high clustering coefficient. Specifically, W-S graphs are generative by randomly rewring edges from regular lattic graphs. The Kronecker Graph model, where graphs are generated in a recursive manner. The key motivation is that real-world graphs often exhibit self-similarity, where the whole structure of the graph has the same shape as its parts. Kronecker graphs are generated by recursively doing Kronecker product over the initiator matrix, which is trained to fit the statistics of the input dataset. We further discuss fast Kronecker generator algorithms. Finally, we show that Kronecker graphs and real graphs are very close in many important graph statistics.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs
For more information about Stanford’s 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 generative…
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generative…
👍5
2022_A_review_of_challenges_and_solutions_in_the_design_and_implementation.pdf
2 MB
📄A review of challenges and solutions in the design and implementation of deep graph neural networks
📘 journal: Artificial Intelligence Review (I.F=0.381)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #review #GNN #implementation
📘 journal: Artificial Intelligence Review (I.F=0.381)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #review #GNN #implementation
👍2👏2
📄Everything is connected: Graph neural networks
📘 journal: Current Opinion in Structural Biology (I.F=6.8)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📘 journal: Current Opinion in Structural Biology (I.F=6.8)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
👍3👏1
📄Graph Representation Learning
📘 Journal: European Symposium on Artificial Neural Networks
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #representation
📘 Journal: European Symposium on Artificial Neural Networks
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #representation
👍2
📄Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence
📘 Journal: International journal of intelligent systems (IF=7)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GCN #overview
📘 Journal: International journal of intelligent systems (IF=7)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GCN #overview
👍3❤1
🎞 Machine Learning with Graphs: Deep Generative Models for Graphs, Graph RNN: Generating Realistic Graphs, Scaling Up & Evaluating Graph Gen, Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥this 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.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥this 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.
📽 Watch: part1 part2 part3 part4
📲Channel: @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 Stanford’s 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 of…
Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types of…
👍3🎉1
2018-Structure-oriented prediction in complex networks.pdf
2.9 MB
📄Structure-oriented prediction in complex networks
📘 Journal: Physics Reports (IF=25.6)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #prediction
📘 Journal: Physics Reports (IF=25.6)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #prediction
👍4
🎞 application of machine learning in traffic optimization
💥Free recorded course by Powel gora
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Powel gora
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Paweł 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 machine…
👍4
📄Do we need deep graph neural networks?
💥Technical paper
💥 One of the hallmarks of deep learning was the use of neural networks with tens or even hundreds of layers. In stark contrast, most of the architectures used in graph deep learning are shallow with just a handful of layers. In this post, I raise a heretical question: does depth in graph neural network architectures bring any advantage?
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #DGNN
💥Technical paper
💥 One of the hallmarks of deep learning was the use of neural networks with tens or even hundreds of layers. In stark contrast, most of the architectures used in graph deep learning are shallow with just a handful of layers. In this post, I raise a heretical question: does depth in graph neural network architectures bring any advantage?
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #DGNN
Medium
Do we need deep graph neural networks?
Is “graph deep learning” a misnomer and is depth useful for graph neural networks?
👍5
📄GCN-tutorial
💥Technical paper
💥 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.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GCN #Coda
💥Technical paper
💥 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.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GCN #Coda
👍3
🎞 pytorch geometric tutorial: graph attention networks implementation
💥Free recorded course
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GAT #code #python
💥Free recorded course
📽 Watch
📲Channel: @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 material…
Outcome:
- Recap
- Introduction
- GAT
- Message Passing pytroch layer
- Simple GCNlayer implementation
- GAT implementation
- GAT Usage
Download the material…
👍2
📄A Review on Graph Neural Network Methods in Financial Applications
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Financial #Applications #review
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Financial #Applications #review
📄Applications of social network analysis in promoting circular economy: a literature review
📘 Published by Vilnius Gediminas Technical University.
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network #review #economy
📘 Published by Vilnius Gediminas Technical University.
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network #review #economy
👍3
🎞 Graph Neural Networks - Lecture 15
💥Free recorded tutorial by Manolis Klis
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN
💥Free recorded tutorial by Manolis Klis
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN
YouTube
Social Network Analysis | Chapter 4 | Link Analysis | Part 2
This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
👍1
🎓Towards a deeper understanding of the Visibility Graph algorithm
📘Master’s Thesis, in the Delft University of Technolog, T.J. Alers
🗓Publish year: 2023
📎Study Thesis
📲Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
📘Master’s Thesis, in the Delft University of Technolog, T.J. Alers
🗓Publish year: 2023
📎Study Thesis
📲Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
👍3👏2
2023_A_survey_of_graph_neural_network_based_recommendation_in_social.pdf
1.6 MB
📄A survey of graph neural network based recommendation in social networks
📘 Journal: Neurocomputing (IF=6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Recommendation #survey
📘 Journal: Neurocomputing (IF=6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Recommendation #survey
👍3👏2