Social media are nowadays one of the main news sources for millions of people around the
globe due to their low cost, easy access and rapid dissemination. This however comes at the …

We present graph wavelet neural network (GWNN), a novel graph convolutional neural
network (CNN), leveraging graph wavelet transform to address the shortcomings of previous …

Entity alignment is the task of linking entities with the same real-world identity from different
knowledge graphs (KGs), which has been recently dominated by embedding-based …

The message passing framework is the foundation of the immense success enjoyed by
graph neural networks (GNNs) in recent years. In spite of its elegance, there exist many …

This work provides the first theoretical study on the ability of graph Message Passing Neural
Networks (gMPNNs)---such as Graph Neural Networks (GNNs)---to perform inductive out-of …

Graph neural networks have recently achieved remarkable success in representing graph-
structured data, with rapid progress in both the node embedding and graph pooling …

In this article, we present a deep learning method to predict the temporal evolution of
dissipative dynamic systems. We propose using both geometric and thermodynamic …

A graph can represent a complex organization of data in which dependencies exist between
multiple entities or activities. Such complex structures create challenges for machine …

Shape analysis tasks, including mesh classification, segmentation, and retrieval
demonstrate symmetries in Euclidean space and should be invariant to geometric …

Temporal networks model a variety of important phenomena involving timed interactions
between entities. Existing methods for machine learning on temporal networks generally …