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13 papers · 1 filter
G2SAT: Learning to Generate SAT Formulas
Jiaxuan You, Haoze Wu, Clark Barrett +2
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, a…
Hyperbolic Graph Convolutional Neural Networks
Ines Chami, Rex Ying, Christopher Ré +1
Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space, which has been shown to incur a large distortion when embedding real-world graphs with scale…
Improving Graph Attention Networks with Large Margin-based Constraints
Guangtao Wang, Rex Ying, Jing Huang +1
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs. GATs learn attention functions that assign weights to nodes so…
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Srijan Kumar, Xikun Zhang, Jure Leskovec
Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, and education. Representation learning presents an at…
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang +4
Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could…
Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (G…