Identify universal building blocks for robust and scalable GNNs.
Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex …
Graph Neural Network architectures for inductive representation learning on arbitrary graphs.
Convolutional neural networks have greatly improved state-of-the-art performances in computer vision and speech analysis tasks, due to its high ability to extract multiple levels of representations of data. In this talk, we are interested in …
Graph-structured data such as social networks, functional brain networks, chemical molecules have brought the interest in generalizing deep learning techniques to graph domains. In this work, we propose an empirical study of neural networks for …
Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to …