Physical pooling functions in graph neural networks for molecular property prediction

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of...

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a...

Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...

Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions

Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...