94 citations · 119 across the 16 of their papers we have counts for
6 papers · 1 filter
Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
Xu Bai, Bin Lu, Kun Zhang +4
Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic prop…
MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data
Jianping Zhou, Bin Lu, Zhanyu Liu +6
Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missin…
OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
Bin Lu, Ze Zhao, Luyu Han +7
Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fa…
Temporal Generalization Estimation in Evolving Graphs
Bin Lu, Tingyan Ma, Xiaoying Gan +4
Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower b…
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Bin Lu, Xiaoying Gan, Ze Zhao +4
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., traini…
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
Bin Lu, Xiaoying Gan, Weinan Zhang +3
Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, so…