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Found 6 papers, 3 papers with code
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free.
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher, it still comes with a high computational cost from large matrix multiplications during training.
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher GNN, it still comes with a high computational cost from large matrix multiplications during training.
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models.
Time-Series Anomaly Detection with Implicit Neural Representation
Detecting anomalies in multivariate time-series data is essential in many real-world applications.
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs.
