📃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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🎞 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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📑Explaining the Explainers in Graph Neural Networks: a Comparative Study

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

🧑‍💻Authors: Antonio Longa, Steve Azzolin, Gabriele Santin, ...
🏢Universities: University of Trento, Italy - Cambridge University, UK

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🎞 Machine Learning with Graphs: GraphSAGE Neighbor Sampling

💥Free recorded course by Prof. Jure Leskovec

💥 This part discussed Neighbor Sampling, That is a representative method used to scale up GNNs to large graphs. The key insight is that a K-layer GNN generates a node embedding by using only the nodes from the K-hop neighborhood around that node. Therefore, to generate embeddings of nodes in the mini-batch, only the K-hop neighborhood nodes and their features are needed to load onto a GPU, a tractable operation even if the original graph is large. To further reduce the computational cost, only a subset of neighboring nodes is sampled for GNNs to aggregate.


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📃A Review of Link Prediction Algorithms in Dynamic Networks

📗 Journal: Mathematics (I.F.=2.3)
🗓 Publish year: 2025

🧑‍💻Authors: Mengdi Sun, Minghu Tang
🏢Universities: Qinghai Minzu University, China

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📃 Graph Neural Network-Based Approaches to Drug Repurposing: A Comprehensive Survey

🗓 Publish year: 2025

🧑‍💻Authors: Alireza A.Tabatabaei, Mohammad Ebrahim Mahdavi, Ehsan Beiranvand, ...
🏢Universities: University of Isfahan, Shahid Beheshti University of Medical Sciences, University of Tehran - Iran

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📲Channel: @Bioinformatics
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📃Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

🗓 Publish year: 2025

🧑‍💻Authors: Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri

🏢Universities: Purdue University, West Lafayette, IN, USA.
Tsinghua University, Beijing, China.

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📃Information diffusion analysis: process, model, deployment, and application

📗 Journal:The Knowledge Engineering Review (I.F.=2.8)
🗓 Publish year: 2025

🧑‍💻Authors: Shashank Sheshar Singh, Divya Srivastava, Madhushi Verma, ...
🏢Universities: Thapar Institute of Engineering & Technology, Bennett University, India

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📄Graph neural networks for single-cell omics data: a review of approaches and applications

📙 Journal: Briefings in Bioinformatics (I.F.=6.8)
🗓 Publish year: 2025

🧑‍💻Authors: Sijie Li, Heyang Hua, Shengquan Chen
🏢Universities: Nankai University, China

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📃A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

🗓 Publish year: 2024
📘Journal: Mechanical Systems and Signal Processing(I.F=7.9)

🧑‍💻Authors: Yucheng Wang, Min Wu, Xiaoli Lia, Lihua Xie and Zhenghua Chen

🏢Universities: Nanyang Technological University, Singapore

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📃A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

🗓 Publish year: 2025

🧑‍💻Authors: Jiaqi HAN, Jiacheng CEN, Liming WU, Zongzhao LI, Xiangzhe KONG, Rui JIAO, Ziyang YU, Tingyang XU, Fandi WU, Zihe WANG, Hongteng XU, Zhewei WEI, Deli ZHAO, Yang LIU, Yu RONG, Wenbing HUANG

🏢Universities: Renmin University of China, Beijing 100872, China,
Stanford University, CA 94305, USA,
Tsinghua University, Beijing 100084, China

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📃 Graph Neural Networks for Vehicular Social Networks: Trends, Challenges, and Opportunities

🗓 Publish year: 2025

🧑‍💻Authors: Elham Binshaflout, Aymen Hamrouni, and Hakim Ghazzai
🏢Universities: Abdullah University of Science and Technology (KAUST), Imam Abdulrahman Bin Faisal University, Saudi Arabia

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📃A Systematic Review of Graph Neural Network in
Healthcare-Based Applications: Recent Advances,
Trends, and Future Directions

🗓 Publish year: 2024

🧑‍💻Authors: Showmick Guha Paul, Arpa Saha, Md. Zahid Hasan, Sheak Rashed Haider Noori, Ahmed Moustafa

🏢Universities: Daffodil International University, Dhaka 1216, Bangladesh.
University of Johannesburg, Auckland Park 2006, South Africa.
Bond University, Gold Coast, QLD 4226, Australia.
Bond University, Gold Coast, QLD 4226, Australia.

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📃 Survey of Graph Neural Network Methods for Dynamic Link Prediction

🗓 Publish year: 2025

🧑‍💻Authors: Nahid Abdolrahmanpour Holagh, Ziad Kobti
🏢University: University of Windsor, Canada

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📃 Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey

🗓Publish year: 2025

🧑‍💻Authors: Payam Zohari & Mostafa Haghir Chehreghani
🏢University: Amirkabir University of Technology (Tehran Polytechnic), Iran

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📲Channel: @Bioinformatics
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