NettetGraph Neural Networks (GNNs) extend neural network models on ubiquitous graph data via utilizing ... To address these issues, we propose RG-Explainer, which adopts reinforcement learning to explain GNNs’ predictions. Our framework is inspired by classic combinatorial optimization solvers, which consists of three crucial steps: ... Nettet1. aug. 2024 · Though graph neural network (GNN) has achieved success in graph representation learning, it is still a challenging task to apply powerful GNN variants to hyper-graphs directly [21, 22].

(PDF) Learning to Explain Graph Neural Networks

NettetA Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of … Nettet5. mar. 2024 · Graph Neural Network. Graph Neural Network, as how it is called, is a neural network that can directly be applied to graphs. It provides a convenient way for … moneyclaim fee

Top 10 Learning Resources for Graph Neural Networks

Nettet10. des. 2024 · Abstract: In recent years, graph neural networks (GNNs) and the research on their explainability are experiencing rapid developments and achieving … Nettet21. jun. 2024 · In this paper, we revisit this problem using graph neural networks (GNNs) to learn P0. We establish a theoretical limit for the identification of P0 in a class of epidemic models. We evaluate our method against different epidemic models on both synthetic and a real-world contact network considering a disease with history and … Nettet1. mar. 2024 · A graph neural network (GNN) is a type of neural network designed to operate on graph-structured data, which is a collection of nodes and edges that … icarus cheat engine talent points