Graph Anomaly Detection (GAD) has surfaced as a significant field of research,
predominantly due to its substantial influence in production environments. Although existing …

Molecular property prediction faces the challenge of limited labeled data as it necessitates a
series of specialized experiments to annotate target molecules. Data augmentation …

Graph neural networks (GNNs) have been successful in a variety of graph-based
applications. Recently, it is shown that capturing long-range relationships between nodes …

The message-passing mechanism helps Graph Neural Networks (GNNs) achieve
remarkable results on various node classification tasks. Nevertheless, the recursive nodes …

We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation
and present different mixup functions for sample pairs and their labels. Mixup is a data …

Inspired by the great success of Deep Neural Networks (DNNs) in natural language
processing (NLP), DNNs have been increasingly applied in source code analysis and …

Developing an effective molecular generation framework even with a limited number of
molecules is often important for its practical deployment, eg, drug discovery, since acquiring …

Existing studies for applying the mixup technique on graphs mainly focus on graph
classification tasks, while the research in node classification is still under-explored. In this …

Few-shot graph classification is a novel yet promising emerging research field that still lacks
the soundness of well-established research domains. Existing works often consider different …

Deep learning has been widely applied in recommender systems, which has achieved
revolutionary progress recently. However, most existing learning-based methods assume …