📄Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
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📄Automated Machine Learning on Graphs: A Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🎞 Machine Learning with Graphs: Neural Subgraph Matching & Counting, Neural Subgraph Matching, Finding Frequent Subgraphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
YouTube
CS224W: Machine Learning with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jR7jK2
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
📄Recent Advances in Network-based Methods for
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Forwarded from Bioinformatics
🎓 Towards causality in gene regulatory network inference
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📄A Survey on Graph Classification and Link Prediction based on GNN
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
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🎓Embedding of Dynamical Networks
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📄Graphs in computer graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics
📄Graph Viz: Exploring, Analyzing, and Visualizing Graphs and Networks with Gephi and ChatGPT
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #ChatGPT
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #ChatGPT
Open Data Science - Your News Source for AI, Machine Learning & more
Graph Viz: Exploring, Analyzing, and Visualizing Graphs and Networks with Gephi and ChatGPT
ChatGPT can do a lot with text, but how can it help with data viz? Here, we look at how you can analyze a global AI community using Gephi and ChatGPT.
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📄Gephi Tutorial: How to use it for Network Analysis?
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
👍3
📄Graph Neural Networks and Their Current Applications in Bioinformatics
📘journal: Frontiers in Genetics (I.F.=3.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#review #Graph_Neural_Networks #Application #Bioinformatics
📘journal: Frontiers in Genetics (I.F.=3.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#review #Graph_Neural_Networks #Application #Bioinformatics
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📄Graph Learning and Its Applications: A Holistic Survey
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Graph #Applications
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Graph #Applications
🎞 Graph Analytics and Graph-based Machine Learning
💥Free recorded course by Clair Sullivan
💥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
💥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…
📕Transportation Network Analysis
🗓Publish year: 2022
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Transportation
🗓Publish year: 2022
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Transportation
🎞 Knowledge Graphs: The Path to Enterprise — Michael Moore and AI Omar Azhar, EY
💥Free recorded tutorial on Knowledge Graphs: A Path to Organization
🔹Michael Moore, Ph.D. — Executive Director, EY Performance Improvement Advisory, Enterprise Knowledge Graphs + AI Lead, EY and Omar Azhar, M.S. — Manager, EY Financial Services Organization Advisory, AI Strategy and Advanced Analytics COE, EY.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graphs #Enterprise
💥Free recorded tutorial on Knowledge Graphs: A Path to Organization
🔹Michael Moore, Ph.D. — Executive Director, EY Performance Improvement Advisory, Enterprise Knowledge Graphs + AI Lead, EY and Omar Azhar, M.S. — Manager, EY Financial Services Organization Advisory, AI Strategy and Advanced Analytics COE, EY.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graphs #Enterprise
YouTube
Knowledge Graphs: The Path to Enterprise — Michael Moore and AI Omar Azhar, EY
Michael Moore, Ph.D. — Executive Director, EY Performance Improvement Advisory, Enterprise Knowledge Graphs + AI Lead, EY and Omar Azhar, M.S. — Manager, EY Financial Services Organization Advisory, AI Strategy and Advanced Analytics COE, EY
📄Graph Theory and Algorithms for Network Analysis
📘Conference: International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Theory #Algorithms
📘Conference: International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Theory #Algorithms
📄Implementation and Analysis of Social Network Graph
in Interpersonal Network
📘 journal: Jurnal Ilmu Komputer (JIK)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Implementation #Graph #Interpersonal_Network
in Interpersonal Network
📘 journal: Jurnal Ilmu Komputer (JIK)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Implementation #Graph #Interpersonal_Network
📄A Review of Graph Neural Networks and Their Applications in Power Systems
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #Applications #Power_Systems #Review
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #Applications #Power_Systems #Review
📕Network Analysis: Integrating Social Network Theory, Method, and Application with R
🗓Publish year: 2023
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Integrating #Method #Application #R
🗓Publish year: 2023
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Integrating #Method #Application #R
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🎞 Workshop: Centrality and Modularity Analysis in Gephi and Visone
💥Workshop (beginner level) on Centrality and Modularity Analysis in Gephi and Visone by Xiong Huei-Lan (Leiden University) & Song Chen (Bucknell University) at the conference "Historical Network Research in Chinese Studies", Day 2 (24.07.2021).
💻Dataset with materials and videos of the conference
🌐Conference website
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Workshop #Centrality #Modularity #Gephi #Visone
💥Workshop (beginner level) on Centrality and Modularity Analysis in Gephi and Visone by Xiong Huei-Lan (Leiden University) & Song Chen (Bucknell University) at the conference "Historical Network Research in Chinese Studies", Day 2 (24.07.2021).
💻Dataset with materials and videos of the conference
🌐Conference website
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Workshop #Centrality #Modularity #Gephi #Visone
YouTube
HNRCS: Xiong Huei-Lan & Song Chen – Workshop: Centrality and Modularity Analysis in Gephi and Visone
Workshop (beginner level) on Centrality and Modularity Analysis in Gephi and Visone by Xiong Huei-Lan (Leiden University) & Song Chen (Bucknell University) at the conference "Historical Network Research in Chinese Studies", Day 2 (24.07.2021)
Dataset with…
Dataset with…
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