📄Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

📘Journal: IEEE ACCESS (I.F=3.476)
🗓Publish year: 2022

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📄Graph Neural Networks for Text Classification: A Survey

🗓Publish year: 2023

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🎞 Graph Neural Networks and Knowledge Graph Completion

💥Free recorded Tutorial by Prof. Soumen Chakrabarti

💥Link prediction in social and knowledge graphs : Permutation Invariant and Time Sensitive Deep Representations

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📄Fairness-Aware Graph Neural Networks: A Survey

🗓Publish year: 2023

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📄A Comprehensive Survey on Graph Neural Networks

🗓Publish year: 2019

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📄Graph Neural Networks and Their Current Applications in Bioinformatics

📘journal: Frontiers in Genetics (I.F.=3.7)
🗓Publish year: 2021

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📄A Review of Graph Neural Networks and Their Applications in Power Systems

🗓Publish year: 2022

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📚 Knowledge Graphs

✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.

🧑‍💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021

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