Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

Graph metric learning methods aim to learn the distance metric over graphs such that similar
(eg, same class) graphs are closer and dissimilar (eg, different class) graphs are farther …

Graph Neural Networks (GNNs) have significantly boosted the performance of many graph-
based applications, yet they serve as black-box models. To understand how GNNs make …

Relations between entities evolving through discrete time-steps can be represented by
discrete-time dynamic graphs. Examples include hourly interactions between social network …

Public health interventions to counter the COVID-19 pandemic have accelerated and
increased digital adoption and use of the Internet for sourcing health information …

In the big data era, the relationship between entries becomes more and more complex.
Many graph (or network) algorithms have already paid attention to dynamic networks, which …

Graph coarsening is a technique for solving large-scale graph problems by working on a
smaller version of the original graph, and possibly interpolating the results back to the …

We propose the Temporal Walk Centrality, which quantifies the importance of a node by
measuring its ability to obtain and distribute information in a temporal network. In contrast to …

Persistent homology theory provides approaches for analyzing topological features, which is
now widely applied in graph comparison on social networks, biological networks, and co …

Abstract Graph Neural Networks (GNNs) have become important machine learning tools for
graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer …