Abstract: Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions between graph ...
Abstract: We introduce a novel approach to feedback stability analysis for linear time-invariant (LTI) systems, overcoming the limitations of the sectoriality assumption in the small phase theorem.
This project provides a massively parallel implementation of a Minimum Spanning Tree (MST) graph algorithm using NVIDIA CUDA. The implementation is based on Borůvka's algorithm, which is highly ...
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