Data-driven reliability models of quantum circuit: From traditional ml to graph neural network
V Saravanan, SM Saeed - IEEE Transactions on Computer …, 2022 - ieeexplore.ieee.org
The current advancement in quantum computers has been focusing on increasing the
number of qubits and enhancing their fidelity. However, the available quantum devices …
number of qubits and enhancing their fidelity. However, the available quantum devices …
EEGNN: Edge enhanced graph neural network with a Bayesian nonparametric graph model
Training deep graph neural networks (GNNs) poses a challenging task, as the performance
of GNNs may suffer from the number of hidden message-passing layers. The literature has …
of GNNs may suffer from the number of hidden message-passing layers. The literature has …
[HTML][HTML] An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network
Y Yue, J Zhang, M Zhang, J Yang - Electronics, 2024 - mdpi.com
Cryptocurrency, as one of the most successful applications of blockchain technology, has
played a vital role in promoting the development of the digital economy. However, its …
played a vital role in promoting the development of the digital economy. However, its …
Bilateral trade flow prediction by gravity-informed graph auto-encoder
The gravity models has been studied to analyze interaction between two objects such as
trade amount between a pair of countries, human migration between a pair of countries and …
trade amount between a pair of countries, human migration between a pair of countries and …
Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance
D Loveland, R Caceres - … Conference on Complex Networks and Their …, 2023 - Springer
Abstract Graph Neural Network (GNN) research has produced strategies to modify a graph's
edges using gradients from a trained GNN, with the goal of network design. However, the …
edges using gradients from a trained GNN, with the goal of network design. However, the …
Predicting stock returns: ARMAX versus machine learning
In the modern world, online social and news media significantly impact society, economy
and financial markets. In this chapter, we compared the predictive performance of financial …
and financial markets. In this chapter, we compared the predictive performance of financial …
Check for Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance Donald Loveland¹ () and Rajmonda Caceres²
D Loveland¹, R Caceres - … & Their Applications XII: Proceedings of …, 2024 - books.google.com
Graph Neural Network (GNN) research has produced strategies to modify a graph's edges
using gradients from a trained GNN, with the goal of network design. However, the factors …
using gradients from a trained GNN, with the goal of network design. However, the factors …