Inspired by dynamic Graph neural networks, we propose a Group-aware Dynamic Graph Representation Learning (GDGRL) method for next POI recommendation. GDGRL connects different user sequences and ...

Although graph embedding is the most popular approach for graph representation learning ... graph topology features from the reconstructed graph. Finally, the linear Logistic Regression (LR) model is ...

To tackle these challenges, we propose a novel framework called AISFuser to i) encode unique maritime traffic network into graphical representations, and ii) introduce the heterogeneity into ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

Arxiv'22 GCDM Graph Condensation via Receptive Field Distribution Matching Mengyang Liu et al. KDD'23 KIDD Kernel Ridge Regression-Based Graph Dataset Distillation Zhe Xu et al. [code] WWW'24 GC-SNTK ...

Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear ...