We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.
In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework.
Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons.
Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i. e. rules that update synapses based on the neuron activations and reinforcement signals.
Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.
Many real-world control and classification tasks involve a large number of features.