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Found 7 papers, 4 papers with code
Maximizing Influence with Graph Neural Networks
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.
Transfer Graph Neural Networks for Pandemic Forecasting
Furthermore, to account for the limited amount of training data, we capitalize on the pandemic's asynchronous outbreaks across countries and use a model-agnostic meta-learning based method to transfer knowledge from one country's model to another's.
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Artificial Intelligence techniques are already popular and important in the legal domain.
Multi-task Learning for Influence Estimation and Maximization
The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors).
A Review of Network Inference Techniques for Neural Activation Time Series
In this work, we perform a review of methods for neural circuit inference given the activation time series of the neural population.
