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Pure Transformers are Powerful Graph Learners
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5
|
191
|
August 17, 2023
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Graph-Bert: Only Attention is Needed for Learning Graph Representations
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0
|
924
|
July 26, 2023
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Attending to Graph Transformers
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0
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1091
|
July 21, 2023
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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
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0
|
483
|
July 7, 2023
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TabPFN: A Transformer That Solves Small Tabular Classification
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0
|
88
|
June 19, 2023
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TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual
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0
|
112
|
June 15, 2023
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Inductive Matrix Completion Based on Graph Neural Networks
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0
|
85
|
June 15, 2023
|
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Complex Embeddings for Simple Link Prediction
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0
|
80
|
June 15, 2023
|
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High-Resolution Image Synthesis with Latent Diffusion Models
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0
|
68
|
June 15, 2023
|
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Do Transformers Really Perform Bad for Graph Representation?
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0
|
115
|
June 15, 2023
|
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Global Self-Attention as a Replacement for Graph Convolution
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0
|
99
|
June 15, 2023
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Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
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0
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61
|
June 15, 2023
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Scalable Graph Neural Networks for Heterogeneous Graphs
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0
|
54
|
June 15, 2023
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Position-based Hash Embeddings For Scaling Graph Neural Networks
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0
|
62
|
June 15, 2023
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Position-aware Graph Neural Networks
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0
|
78
|
June 15, 2023
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Graph Neural Networks with Learnable Structural and Positional Representations
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0
|
95
|
June 15, 2023
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Inductive Graph Embeddings through Locality Encodings
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0
|
120
|
June 15, 2023
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