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¦
π3
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
β€2
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
π3β€1
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
π4
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
π4
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 ...
π6π1
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
π3
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.
π3
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
π4
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
π4π1
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
π6
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¦
π4
πDeep Learning on Graphs: A Survey
π Journal: IEEE Transactions on Knowledge and Data Engineering
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
π Journal: IEEE Transactions on Knowledge and Data Engineering
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
π4
Network_and_Content_Analysis_in_an_Online_Community_Discourse.pdf
292.2 KB
πNetwork and Content Analysis in an Online Community Discourse
π₯The aim of this paper is to study interaction patterns among the members of a community of practice within the Dutch police organization and the way they share and construct knowledge together. The online discourse between 46 members, using First Class, formed the basis for this study. Social Network Analysis and content analysis were used to analyze the data. The results show that the interaction patterns between the members are rather centralized and that the network is relatively dense. Most of the members are involved within the discourse but person to person communication is still rather high. Content analysis revealed that discourse is focused on sharing and comparing information.
π Read online
π²Channel: @ComplexNetworkAnalysis
#book_Chapter
π₯The aim of this paper is to study interaction patterns among the members of a community of practice within the Dutch police organization and the way they share and construct knowledge together. The online discourse between 46 members, using First Class, formed the basis for this study. Social Network Analysis and content analysis were used to analyze the data. The results show that the interaction patterns between the members are rather centralized and that the network is relatively dense. Most of the members are involved within the discourse but person to person communication is still rather high. Content analysis revealed that discourse is focused on sharing and comparing information.
π Read online
π²Channel: @ComplexNetworkAnalysis
#book_Chapter
π4
πA Literature Review on Graph Neural Networks
π₯Free recorded video on A Literature Review on Graph Neural Networks.
π first paper
π second paper
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #GNN #review
π₯Free recorded video on A Literature Review on Graph Neural Networks.
π first paper
π second paper
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #GNN #review
YouTube
A Literature Review on Graph Neural Networks
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-04-15-spotlight
Discussion lead: Nabila Abraham
Survey papers:
1. Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
2. Representationβ¦
Discussion lead: Nabila Abraham
Survey papers:
1. Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
2. Representationβ¦
π4
π Machine Learning with Graphs: Applications of Deep Graph Generation.
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π½ Watch
π²Channel: @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 Stanfordβs 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
Finallyβ¦
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finallyβ¦
π5β€1
πRecommending on graphs: a comprehensive review from a data perspective
π Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
π Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
β€2π1
π Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
π₯Free recorded course 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 #course #Graph #GNN #code #python #tensorflow
π₯Free recorded course 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 #course #Graph #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¦
π4
πSocial network research in the family business literature: a review and integration
π Journal: Small Business Economics (I.F=6.4)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
π Journal: Small Business Economics (I.F=6.4)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
π2