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Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) on graph-structured data, especially on node-level tasks.
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Relational inductive biases, deep learning, and graph networks
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Homophily influences ranking of minorities in social networks
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Modeling text with graph convolutional network for cross-modal information retrieval
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Revisiting graph neural networks: All we have is low-pass filters
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Rethinking table recognition using graph neural networks
Qasim, S. R., Mahmood, H., and Shafait, F · 2019
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Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data
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Comprehensive integration of single-cell data
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Simplifying graph convolutional networks
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Graph transformer networks
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On the bottleneck of graph neural networks and its practical implications
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Node embeddings and exact low-rank representations of complex networks
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Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding
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Deep learning for 3d point clouds: A survey
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Learning to simulate complex physics with graph networks
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The impossibility of low-rank representations for triangle-rich complex networks
Seshadhri, C., Sharma, A., Stolman, A., and Goel, A · 2020
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Graph neural networks in particle physics
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Multi-hop attention graph neural network
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Topology uncertainty modeling for imbalanced node classification on graphs
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Homophily-enhanced structure learning for graph clustering
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Graph neural networks for binary programming
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Heterophily-aware fair recommendation using graph convolutional networks
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Prompt tuning for multi-view graph contrastive learning
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