Graph deep learning has recently emerged as a powerful ML concept allowing to generalize
successful deep neural architectures to non-euclidean structured data. Such methods have …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

In computer-aided drug discovery, quantitative structure activity relation models are trained
to predict biological activity from chemical structure. Despite the recent success of applying …

Graph data are pervasive in real-world scenarios, and research on graph data has become
a research hotspot. Over the past few decades, significant advancements have been made …

Graph neural networks are increasingly becoming the framework of choice for graph-based
machine learning. In this paper, we propose a new graph neural network architecture that …

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data
ubiquitous in the healthcare domain. Two prominent examples are molecule property …

Topological Data Analysis (TDA) has been praised by researchers for its ability to capture
intricate shapes and structures within data. TDA is considered robust in handling noisy and …

This work considers the task of representation learning on the attributed relational graph
(ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing …

Abstract Graph Convolutional Networks (GCNs) with naive message passing mechanisms
have limited performance due to the isotropic aggregation strategy. To remedy this …

Managing the threat posed by malware requires accurate detection and classification
techniques. Traditional detection strategies, such as signature scanning, rely on manual …