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 | AIM
A graph attention network can be explained as leveraging the attention mechanism in the graph neural networks so that we can address some of the shortcomings of the graph neural networks.
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
πGenerative Diffusion Models on Graphs: Methods and Applications
π CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
π CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
π7
πGraph neural networks for materials science and
chemistry
π Journal: Communications Materials (I.F=7.8)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
chemistry
π Journal: Communications Materials (I.F=7.8)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
π2
πNetwork Medicine in Pathobiology
π journal: The American Journal of Pathology(I.F=5.1)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
π journal: The American Journal of Pathology(I.F=5.1)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
π6
πA Survey on the Recent Advances of Deep Community Detection
π Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
π Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
π4
πMolecular networks in Network Medicine
π Journal: WILEY (I.F=5.609)
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Molecular_networks #Medicine
π Journal: WILEY (I.F=5.609)
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Molecular_networks #Medicine
π4β€1
πA comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations
π Journal: BRIEFINGS IN BIOINFORMATICS (I.F=10.6)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #non_coding #RNA #complex_disease #review
π Journal: BRIEFINGS IN BIOINFORMATICS (I.F=10.6)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #non_coding #RNA #complex_disease #review
π3
πGraph Attention Networks
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
π₯Technical paper
π Study
π²Channel: @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.
π4