Abstract: In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections ...
Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...
This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results