Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Dealing with missing values and incomplete time series is a labor-intensive, tedious,
inevitable task when handling data coming from real-world applications. Effective spatio …

Spatiotemporal graph neural networks have shown to be effective in time series forecasting
applications, achieving better performance than standard univariate predictors in several …

Time-series and graphs have been extensively studied for their ubiquitous existence in
numerous domains. Both topics have been separately explored in the field of deep learning …

Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory.
Given a graph pair (G 1, G 2), GED is defined as the minimum number of primitive …

This article aims to unify spatial dependency and temporal dependency in a non-Euclidean
space while capturing the inner spatial–temporal dependencies for traffic data. For spatial …

Recent progress in research on deep graph networks (DGNs) has led to a maturation of the
domain of learning on graphs. Despite the growth of this research field, there are still …

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of
nodes in graphs, has received significant attention. In recent years, there has been a surge …

Information extraction (IE) is an important task in Natural Language Processing (NLP),
involving the extraction of named entities and their relationships from unstructured text. In …

Deep neural networks require specific layers to process point clouds, as the scattered and
irregular location of points prevents us from using convolutional filters. Here we introduce …