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Found 5 papers, 4 papers with code
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.
Ranked #1 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
Predicting Human Mobility via Self-supervised Disentanglement Learning
However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions.
CCGL: Contrastive Cascade Graph Learning
Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.
Ranked #1 on
Information Cascade Popularity Prediction
on Weibo
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.
A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.
