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Graph Neural Networks based on the message-passing (MP) mechanism are a dominant approach for handling graph-structured data.
CiteSeer: An automatic citation indexing system
C. Lee Giles, Kurt D. Bollacker, and Steve Lawrence · 1998
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Automating the Construction of Internet Portals with Machine Learning
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The Graph Neural Network Model
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Neural Message Passing for Quantum Chemistry, June 2017
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Semi-Supervised Classification with Graph Convolutional Networks, February 2017
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Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, January 2018
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
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KGAT: Knowledge Graph Attention Network for Recommendation
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A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces, December 2023
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Recurrent Distance-Encoding Neural Networks for Graph Representation Learning
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Topological Deep Learning: Going Beyond Graph Data, May 2023
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, and Michael T. Schaub · 2023
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Topological Signal Processing over Simplicial Complexes
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Residual Correlation in Graph Neural Network Regression
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Highly accurate protein structure prediction with AlphaFold
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Simplicial Complex Neural Networks
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Convolutional Learning on Simplicial Complexes, January 2023
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Graph Mamba: Towards Learning on Graphs with State Space Models, February 2024
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Position Paper: Challenges and Opportunities in Topological Deep Learning
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Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks, February 2024
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Prediction of protein–protein interaction using graph neural networks
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