📄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
🔥4👍2👏1
📄A Survey on Hyperlink Prediction
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Hyperlink #prediction #survey
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Hyperlink #prediction #survey
👍2🔥1
📄A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions
📘 Journal: Journal of Big Data (I.F=10.835)
🗓Publish year: 2024
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #GraphSage #GAT #Survey
📘 Journal: Journal of Big Data (I.F=10.835)
🗓Publish year: 2024
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #GraphSage #GAT #Survey
🔥4👍2
📄A survey on deep learning based Point-of-Interest (POI) recommendations
📘 Journal: Neurocomputing (I.F= 6)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep_learning #POI #recommendation #survey
📘 Journal: Neurocomputing (I.F= 6)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep_learning #POI #recommendation #survey
👍2🤩1
📄Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
📘 Journal: Electronics (I.F=10.835)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Deep_Learning #Review
📘 Journal: Electronics (I.F=10.835)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Deep_Learning #Review
❤3👍2
📄A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions
📘 Journal: J BIG DATA-GER (I.F= 8.1)
🗓Publish year: 2024
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #architectures #applications #future #review
📘 Journal: J BIG DATA-GER (I.F= 8.1)
🗓Publish year: 2024
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #architectures #applications #future #review
👍3
📄Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Biomedicine #GRL
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Biomedicine #GRL
🔥2👍1
📄A Survey on Hypergraph Mining: Patterns, Tools, and
Generators
🗓Publish year: 2024
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Hypergraph #Patterns #Tools #Generators #Survey
Generators
🗓Publish year: 2024
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Hypergraph #Patterns #Tools #Generators #Survey
👍2
📄Network embedding: Taxonomies, frameworks and applications
📘 Journal: Computer Science Review (I.F= 12.9)
🗓Publish year: 2020
👩🎓Authors: Mingliang Hou (Dalian University of Technology), Jing Ren (Dalian University of Technology), Da Zhang (University of Miami), Xiangjie Kong (Zhejiang University of Technology), Dongyu Zhang (Dalian University of Technology), Feng Xia (Federation University Australia)
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #embedding #Taxonomies #frameworks #applications
📘 Journal: Computer Science Review (I.F= 12.9)
🗓Publish year: 2020
👩🎓Authors: Mingliang Hou (Dalian University of Technology), Jing Ren (Dalian University of Technology), Da Zhang (University of Miami), Xiangjie Kong (Zhejiang University of Technology), Dongyu Zhang (Dalian University of Technology), Feng Xia (Federation University Australia)
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #embedding #Taxonomies #frameworks #applications
👍1
📃 A Review of Link Prediction Applications in Network Biology
🗓 Publish year: 2023
🧑💻Authors: Ahmad F. Al Musawi, Satyaki Roy, Preetam Ghosh
🏢Universities: Virginia Commonwealth University, University of Alabama in Huntsville
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#review #Network_Biology #Link_Prediction
🗓 Publish year: 2023
🧑💻Authors: Ahmad F. Al Musawi, Satyaki Roy, Preetam Ghosh
🏢Universities: Virginia Commonwealth University, University of Alabama in Huntsville
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#review #Network_Biology #Link_Prediction
👍5❤3👏1