π Graph Neural Networks - Lecture 15
π₯Free recorded tutorial by Manolis Klis
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #GNN
π₯Free recorded tutorial by Manolis Klis
π½ Watch
π±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
πTowards a deeper understanding of the Visibility Graph algorithm
πMasterβs Thesis, in the Delft University of Technolog, T.J. Alers
πPublish year: 2023
πStudy Thesis
π²Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
πMasterβs Thesis, in the Delft University of Technolog, T.J. Alers
πPublish year: 2023
πStudy Thesis
π²Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
2023_A_survey_of_graph_neural_network_based_recommendation_in_social.pdf
1.6 MB
πA survey of graph neural network based recommendation in social networks
π Journal: Neurocomputing (IF=6)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Recommendation #survey
π Journal: Neurocomputing (IF=6)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Recommendation #survey
πA Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
π Journal: Electronics (IF=2.9)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Techniques #Application #survey
π Journal: Electronics (IF=2.9)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Techniques #Application #survey
π Analysis on Collaboration and Co-Authorship Network using Centrality Measures
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Co_Authorship #Centrality
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Co_Authorship #Centrality
YouTube
Analysis on Collaboration and Co-Authorship Network using Centrality Measures
This is a presentation of a mini-paper I wrote on analysis on collaboration and co-authorship network of international Network Science researches by using the classical centrality measures and structural holes. The data set I used here is from M.E.J. Newmanβ¦
πA Review on Graph Neural Network Methods in Financial Applications
π Journal: Mental Health and Social Inclusion (IF=1.2)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Financial #Application #review
π Journal: Mental Health and Social Inclusion (IF=1.2)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Financial #Application #review
π Co-Authorship Network Analysis using GEPHI
π₯This video is a part of one of the research articles that analyzes the collaboration patterns of the scientific co-authored article.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Co_Authorship #GEPHI
π₯This video is a part of one of the research articles that analyzes the collaboration patterns of the scientific co-authored article.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Co_Authorship #GEPHI
YouTube
Analysis on Collaboration and Co-Authorship Network using Centrality Measures
This is a presentation of a mini-paper I wrote on analysis on collaboration and co-authorship network of international Network Science researches by using the classical centrality measures and structural holes. The data set I used here is from M.E.J. Newmanβ¦
π Understanding Graph Attention Networks
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #GNN #GAT #Graph
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #GNN #GAT #Graph
YouTube
Understanding Graph Attention Networks
β¬β¬ Resources β¬β¬β¬β¬β¬β¬β¬β¬β¬β¬
Paper: https://arxiv.org/pdf/1710.10903.pdf
Attention in NLP YouTube Series: https://www.youtube.com/watch?v=yGTUuEx3GkA (Rasa)
β¬β¬ Used Music β¬β¬β¬β¬β¬β¬β¬β¬β¬β¬β¬
Field Of Fireflies by Purrple Cat | https://purrplecat.com
Music promotedβ¦
Paper: https://arxiv.org/pdf/1710.10903.pdf
Attention in NLP YouTube Series: https://www.youtube.com/watch?v=yGTUuEx3GkA (Rasa)
β¬β¬ Used Music β¬β¬β¬β¬β¬β¬β¬β¬β¬β¬β¬
Field Of Fireflies by Purrple Cat | https://purrplecat.com
Music promotedβ¦
πThe Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Dimensions #Methods #Application #Software #Tools #Overview
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Dimensions #Methods #Application #Software #Tools #Overview
πGraph Representation Learning
π₯Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.
π Read online
π²Channel: @ComplexNetworkAnalysis
#book #GRL #GNN
π₯Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.
π Read online
π²Channel: @ComplexNetworkAnalysis
#book #GRL #GNN
πA Review of Link Prediction Applications in Network Biology
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Application #Biology #review
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Application #Biology #review
πA study of visibility graphs for time series representations
πBachelorβs Thesis, in the University Polytechnica de catalunya barcelonatech, Bergillos Varela, Carlos
πPublish year: 2020
πStudy Thesis
π²Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
πBachelorβs Thesis, in the University Polytechnica de catalunya barcelonatech, Bergillos Varela, Carlos
πPublish year: 2020
πStudy Thesis
π²Channel: @ComplexNetworkAnalysis
#Thesis #Visibility_Graph
π Promise and perils of population-scale social network analysis
π₯Free recorded presentation by Frank Takes.
π₯A relatively recently emerging line of research is devoted to the use of large-scale population register data to answer enduring questions in the realm of social science. In this presentation, it specifically delves into the network dimension of such data, focusing on information from the POPNET project, which covers more than 17 million people (i.e., the entire population of the Netherlands) and approximately 800 million family, household, school, work, and neighbor-to-neighbor connections. The presentation highlights the potential inherent in this comprehensive and curated social network data through illustrative examples of results related to issues such as social capital, segregation, and migration. Additionally, it will examine several methodological considerations and challenges related to under- and over-sampling of individual connections within opportunity structures, including findings on the validity of real-world skewed degree distributions.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Promise #perils #population_scale
π₯Free recorded presentation by Frank Takes.
π₯A relatively recently emerging line of research is devoted to the use of large-scale population register data to answer enduring questions in the realm of social science. In this presentation, it specifically delves into the network dimension of such data, focusing on information from the POPNET project, which covers more than 17 million people (i.e., the entire population of the Netherlands) and approximately 800 million family, household, school, work, and neighbor-to-neighbor connections. The presentation highlights the potential inherent in this comprehensive and curated social network data through illustrative examples of results related to issues such as social capital, segregation, and migration. Additionally, it will examine several methodological considerations and challenges related to under- and over-sampling of individual connections within opportunity structures, including findings on the validity of real-world skewed degree distributions.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Promise #perils #population_scale
YouTube
Frank Takes - Promise and perils of population-scale social network analysis
Frank Takes, Leiden University
A relatively recently emerged line of research is dedicated to harnessing large-scale population register data to address enduring questions within the realm of social science. In this presentation, we will specifically delveβ¦
A relatively recently emerged line of research is dedicated to harnessing large-scale population register data to address enduring questions within the realm of social science. In this presentation, we will specifically delveβ¦
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 ...
πLink Prediction in Social Networks: A Bibliometric Analysis and Review of Literature (1987-2021)
π Journal: Journal of Artificial Intelligence & Data Mining
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Bibliometric #review
π Journal: Journal of Artificial Intelligence & Data Mining
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Bibliometric #review
πAll you need to know about Graph Attention Networks
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Coda
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Coda
Analytics India Magazine
All you need to know about Graph Attention Networks
A graph attention network is also a type of graph neural network that applies an attention mechanism to itself.
πA SURVEY OF GRAPH UNLEARNING
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Unlearning #Survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Unlearning #Survey
πTheory of Graph Neural Networks: Representation and Learning
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #GRL
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #GRL
πFrom Graph Theory to Graph Neural Networks
(GNNs): The Opportunities of GNNs in Power Electronics
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
(GNNs): The Opportunities of GNNs in Power Electronics
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
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¦