Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new
architectures as well as novel applications. However, current research focuses on proposing …

Abstract Message passing Graph Neural Networks (GNNs) provide a powerful modeling
framework for relational data. However, the expressive power of existing GNNs is upper …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

Thanks to the increasing availability of genomics and other biomedical data, many machine
learning algorithms have been proposed for a wide range of therapeutic discovery and …

Missing data is an important problem in machine learning practice. Starting from the premise
that imputation methods should preserve the causal structure of the data, we develop a …

Neural methods have become the de-facto choice for the vast majority of data analysis tasks,
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …