📃A Comparison of Graph Construction Methods for Semi-Supervised Learning

📘Conference: 2018 International Joint Conference on Neural Networks (IJCNN)
🗓Publish year: 2018

🧑‍💻Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
🏢University: University of São Paulo

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#Paper #Semi_Supervised #network

📃Research Output, Key Topics, and Trends in Productivity, Visibility, and Collaboration in Social Sciences Research on COVID-19: A Scientometric Analysis and Visualization

🗓 Publish year: 2024
📘Journal: SAGE Open (I.F=2)

🧑‍💻Authors: Walaa Hamdan, Hanan Alsuqaih
🏢Universities: College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Airport Road, P.O.Box 84428, Riyadh 11671, Saudi Arabia.

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#paper #Visibility #Collaboration #Productivity #Scientometric #COVID_19 #Visualization

🎞 Link Prediction with GraphSage explained | step-by-step

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#video #link_prediction #gnn

📃Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review

🗓 Publish year: 2024
📘Journal: BMC Health Services Research (I.F=2.7)

🧑‍💻Authors: Troy Francis، Morgan Davidson، Laura Senese، Lianne Jeffs، Reza Yousefi-Nooraie، Mathieu Ouimet، Valeria Rac، Patricia Trbovich
🏢Universities: University of Toronto, Toronto, Canada.
North York General Hospital, North York, Canada.
University Health Network, Toronto, ON, Canada.
University of Rochester, New York, USA.

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#paper #healthcare #organizations #review

📑Can Graph Neural Networks be Adequately Explained? A Survey

📗 Journal: ACM Computing Surveys (🔥I.F.=23.8)
🗓 Publish year: 2025

🧑‍💻Authors: Xuyan Li, Jie Wang, Zheng Yan
🏢University: Xidian University, China

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#review #gnn #explainability

🎞 Machine Learning with Graphs: hyperbolic graph embeddings

💥Free recorded course by Prof. Jure Leskovec

💥 This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks

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#video #course #Graph #GNN #Machine_Learning

🎞 Intro to Graph Analytics in Python

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#video #graph #python

📃Understanding When and Why Graph Attention Mechanisms Work via Node Classification

🗓Publish year: 2024

🧑‍💻Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
🏢University: Northwestern Polytechnical University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiaotong University

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#Paper #GAT #Attention #node_classification

📃A comprehensive survey on graph neural network accelerators

🗓 Publish year: 2025
📘Journal: Frontiers of Computer Science (I.F=3.4)

🧑‍💻Authors: Jingyu LIU, Shi CHEN, Li SHEN
🏢Universities: School of Computer, National University of Defense Technology, Changsha 410073, China

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📄 A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions

📕 Journal: Knowledge and Information System (I.F.=2.5)
🗓 Publish year: 2025

🧑‍💻Authors: Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun,...
🏢University:
- University of Technology Sydney, Ultimo, Australia
- Urmia University, Urmia &Iran University of Science and Technology, Iran

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#review #anonymization

📃A Survey on Graph Neural Networks and its Applications in Various Domains

🗓Publish year: 2025

🧑‍💻Authors: Tejaswini R. Murgod, P. Srihith Reddy, Shamitha Gaddam, S. Meenakshi Sundaram & C. Anitha
🏢University: BNM Institute of Technology, NITTE Meenakshi Institute of Technology,

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📄 Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact through Machine Learning

📗 Journal: IEEE ACCESS (I.F.=3.4)
🗓 Publish year: 2025

🧑‍💻Authors: D. Plikynas, I. Rizgelienė, G. Korvel,...
🏢University: Vilnius university, Vilnius, Lithuania

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#review #fake_news

🎬 Inferring Biological Networks
💥 from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison

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📲Channel: @Bioinformatics
#video #network

📃Link Prediction in Social Networks: A Review

🗓 Publish year: 2024
📘Conference: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)

🧑‍💻Authors: Meghana Sreeya Veeramallu, Harshitha Reddy Mallu, Ramadasu B
🏢Universities: Chaitanya Bharathi Institute of Technology, India

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🎬 Random Graphs

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▫️Part 1
▫️Part 2

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🎞 Graph Coloring Problem Explained

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🎞 Graph Neural Networks

💥presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School

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🎞 Machine Learning with Graphs: design space of graph neural networks

💥Free recorded course by Prof. Jure Leskovec

💥 This part discussed the important topic of GNN architecture design. Here, we introduce 3 key aspects in GNN design: (1) a general GNN design space, which includes intra-layer design, inter-layer design and learning configurations; (2) a GNN task space with similarity metrics so that we can characterize different GNN tasks and, therefore, transfer the best GNN models across tasks; (3) an effective GNN evaluation technique so that we can convincingly evaluate any GNN design question, such as “Is BatchNorm generally useful for GNNs?”. Overall, we provide the first systematic investigation of general guidelines for GNN design, understandings of GNN tasks, and how to transfer the best GNN designs across tasks. We release GraphGym as an easy-to-use code platform for GNN architectural design. More information can be found in the paper: Design Space for Graph Neural Networks

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#video #course #Graph #GNN #Machine_Learning