📚 A Simple Introduction to Graph Theory
💥Booklet
🗓Publish year: 2024
🧑💻Author: Brian Heinold
🏢University: Mount Saint Mary's University, USA
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⚡️Channel: @ComplexNetworkAnalysis
#book #booklet #graph
💥Booklet
🗓Publish year: 2024
🧑💻Author: Brian Heinold
🏢University: Mount Saint Mary's University, USA
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⚡️Channel: @ComplexNetworkAnalysis
#book #booklet #graph
👍1
📑 Demystifying the Power of Large Language Models in Graph Structure Generation
🗓 Publish year: 2025
🧑💻Author: Yu Wang, Ryan Rossi, Namyong Park, ...
🏢University: University of Oregon, Adobe Research, Cisco AI Research, University of Michigan, USA
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#chatgpt #llm #graph_generation
🗓 Publish year: 2025
🧑💻Author: Yu Wang, Ryan Rossi, Namyong Park, ...
🏢University: University of Oregon, Adobe Research, Cisco AI Research, University of Michigan, USA
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⚡️Channel: @ComplexNetworkAnalysis
#chatgpt #llm #graph_generation
👍1
📄 Graph Foundation Models: A Comprehensive Survey
🗓 Publish year: 2025
🧑💻Authors: Zehong Wang, Zheyuan Liu, Tianyi Ma, ...
🏢Universities: University of Notre Dame, University of Connecticut, University of Virginia,
University of Illinois Urbana-Champaign, USA - University of Cambridge, England
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📦 GitHub Resources
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#review #Graph_Foundation_Models #llm
🗓 Publish year: 2025
🧑💻Authors: Zehong Wang, Zheyuan Liu, Tianyi Ma, ...
🏢Universities: University of Notre Dame, University of Connecticut, University of Virginia,
University of Illinois Urbana-Champaign, USA - University of Cambridge, England
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📦 GitHub Resources
⚡️Channel: @ComplexNetworkAnalysis
#review #Graph_Foundation_Models #llm
📃Graph Learning for Fake Review Detection
🗓 Publish year: 2022
📘Journal: Frontiers in Artificial Intelligence(I.F=3)
🧑💻Authors: Shuo Yu, Jing Ren, Shihao Li, Mehdi Naseriparsa, Feng Xia
🏢Universities: Dalian University of Technology, China.
Federation University Australia, Australia.
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#paper #Graph #Fake #Detection #review
🗓 Publish year: 2022
📘Journal: Frontiers in Artificial Intelligence(I.F=3)
🧑💻Authors: Shuo Yu, Jing Ren, Shihao Li, Mehdi Naseriparsa, Feng Xia
🏢Universities: Dalian University of Technology, China.
Federation University Australia, Australia.
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Fake #Detection #review
❤1👍1
Forwarded from Bioinformatics
📑 Graph Neural Networks in Modern AI-aided Drug Discovery
🗓Publish year: 2025
🧑💻Authors: Odin Zhang, Haitao Lin, Xujun Zhang, ...
🏢Universities: Zhejiang University, Hangzhou & Westlake University, China - Harvard University, USA
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📲Channel: @Bioinformatics
#review #drug #ai #gnn #graph_neural_network
🗓Publish year: 2025
🧑💻Authors: Odin Zhang, Haitao Lin, Xujun Zhang, ...
🏢Universities: Zhejiang University, Hangzhou & Westlake University, China - Harvard University, USA
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📲Channel: @Bioinformatics
#review #drug #ai #gnn #graph_neural_network
🎥 Graph Classification: Step-by-step in action
🎞 Watch
⚡️Channel: @ComplexNetworkAnalysis
#video #classification #graph
🎞 Watch
⚡️Channel: @ComplexNetworkAnalysis
#video #classification #graph
YouTube
04 - Graph Classification | step-by-step
0:00 Data preparation
8:50 GNN with GCN
22:00 GNN with SageConv
In this video, we’ll be exploring the implementation of graph classification models using SAGEConv and GCN.
We'll be working with the GIN dataset, which includes 1113 graphs spread across 2…
8:50 GNN with GCN
22:00 GNN with SageConv
In this video, we’ll be exploring the implementation of graph classification models using SAGEConv and GCN.
We'll be working with the GIN dataset, which includes 1113 graphs spread across 2…
👍3
📹 Introduction to Graph Machine Learning: Methods and Applications
💥 Jundong Li, Associate Professor, University of Virginia
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⚡️Channel: @ComplexNetworkAnalysis
#video #graph_machine_learning
💥 Jundong Li, Associate Professor, University of Virginia
🎞 Watch
⚡️Channel: @ComplexNetworkAnalysis
#video #graph_machine_learning
YouTube
Introduction to Graph Machine Learning: Methods and Applications
To get the slides and a certificate of completion, go to https://academy.isdsa.org/moodle/course/view.php?id=26
Graph-structured data is central to many real-world problems, encompassing domains such as recommender systems, social network analysis, and computational…
Graph-structured data is central to many real-world problems, encompassing domains such as recommender systems, social network analysis, and computational…
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📄 Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
🗓 Publish year: 2025
🧑💻Authors: Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, ...
🏢Universities: Clemson University, USA - University of New England & UNSW Sydney, Australia - Chinese Academy of Sciences, China.
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#review #multimodal #biomedical #interpretable #graph_machine_learning #explainability
🗓 Publish year: 2025
🧑💻Authors: Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, ...
🏢Universities: Clemson University, USA - University of New England & UNSW Sydney, Australia - Chinese Academy of Sciences, China.
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#review #multimodal #biomedical #interpretable #graph_machine_learning #explainability
👍2
📄 Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
📙 Journal: ACM Computing Surveys (🔥I.F.=28)
🗓 Publish year: 2025
🧑💻Authors: Zhiqiang Zhong, Anastasia Barkova & Davide Mottin
🏢Universities: Aarhus Universitet, Denmark & WhiteLabGenomics, France
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#review #graph #machine_learning #drug
📙 Journal: ACM Computing Surveys (🔥I.F.=28)
🗓 Publish year: 2025
🧑💻Authors: Zhiqiang Zhong, Anastasia Barkova & Davide Mottin
🏢Universities: Aarhus Universitet, Denmark & WhiteLabGenomics, France
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⚡️Channel: @ComplexNetworkAnalysis
#review #graph #machine_learning #drug
❤2