📄From Graph Theory to Graph Neural Networks
(GNNs): The Opportunities of GNNs in Power Electronics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
(GNNs): The Opportunities of GNNs in Power Electronics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
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🎞Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
💥Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
💥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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
💥Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
💥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.
📽 Watch
📲Channel: @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 Neural…
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
👍4
📄Deep Learning on Graphs: A Survey
📘 Journal: IEEE Transactions on Knowledge and Data Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
📘 Journal: IEEE Transactions on Knowledge and Data Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
👍4
Network_and_Content_Analysis_in_an_Online_Community_Discourse.pdf
292.2 KB
📕Network and Content Analysis in an Online Community Discourse
💥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.
🌐 Read online
📲Channel: @ComplexNetworkAnalysis
#book_Chapter
💥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.
🌐 Read online
📲Channel: @ComplexNetworkAnalysis
#book_Chapter
👍4
🎞A Literature Review on Graph Neural Networks
💥Free recorded video on A Literature Review on Graph Neural Networks.
📄 first paper
📄 second paper
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #review
💥Free recorded video on A Literature Review on Graph Neural Networks.
📄 first paper
📄 second paper
📽 Watch
📱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. Representation…
Discussion lead: Nabila Abraham
Survey papers:
1. Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
2. Representation…
👍4
🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @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 Stanford’s 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
Finally…
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
👍5❤1
📄Recommending on graphs: a comprehensive review from a data perspective
📘 Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
📘 Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
❤2👏1
🎞 Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
💥Free recorded course by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
💥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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #code #python #tensorflow
💥Free recorded course by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
💥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.
📽 Watch
📲Channel: @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 Neural…
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
👍4
📄Social network research in the family business literature: a review and integration
📘 Journal: Small Business Economics (I.F=6.4)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
📘 Journal: Small Business Economics (I.F=6.4)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
👏2
📄Generative Diffusion Models on Graphs: Methods and Applications
📘 CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
📘 CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
👍7
📄Graph neural networks for materials science and
chemistry
📘 Journal: Communications Materials (I.F=7.8)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
chemistry
📘 Journal: Communications Materials (I.F=7.8)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
👍2
📄Network Medicine in Pathobiology
📘 journal: The American Journal of Pathology(I.F=5.1)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
📘 journal: The American Journal of Pathology(I.F=5.1)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
👍6
📄A Survey on the Recent Advances of Deep Community Detection
📘 Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
📘 Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
👍4
📄Molecular networks in Network Medicine
📘 Journal: WILEY (I.F=5.609)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Molecular_networks #Medicine
📘 Journal: WILEY (I.F=5.609)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Molecular_networks #Medicine
👍4❤1
📄A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations
📘 Journal: BRIEFINGS IN BIOINFORMATICS (I.F=10.6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #non_coding #RNA #complex_disease #review
📘 Journal: BRIEFINGS IN BIOINFORMATICS (I.F=10.6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #non_coding #RNA #complex_disease #review
👍3
📄Graph Attention Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network
💥Technical paper
🌐 Study
📲Channel: @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.
👍4
📄A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Time_Series #Forecasting #Classification #Imputation #Anomaly_Detection #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Time_Series #Forecasting #Classification #Imputation #Anomaly_Detection #survey
👍4
🎞 Machine Learning with Graphs: Generative Models for Graphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Generative_Models
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In 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.
📽 Watch
📲Channel: @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 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…
👍6
🎞 Graph Analytics and Graph-based Machine Learning
💥Free recorded course by Clair Sullivan(Neo4j)
💥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 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Clair Sullivan(Neo4j)
💥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 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.
📽 Watch
📲Channel: @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 data…
👍4👏1
📄What Are Higher-Order Networks?
📘 Journal: SIAM Review
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Higher_Order_Networks
📘 Journal: SIAM Review
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Higher_Order_Networks
👍2
📄Machine Learning for Refining Knowledge Graphs: A Survey
📘 Journal: acm digital library (I.F=14.324)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
📘 Journal: acm digital library (I.F=14.324)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
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