Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Graph Neural Networks (GNNs) are popular for graph machine learning and have shown
great results on wide node classification tasks. Yet, they are less popular for practical …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the
majority within a graph, has garnered significant attention. However, current GAD methods …

Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent
years, causing massive financial losses. Rule-based detection approaches rely on pre …

We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal
tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch …

Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent
years, causing massive financial losses. Existing detection methods primarily focus on rule …

Graphs can model complicated interactions between entities, which naturally emerge in
many important applications. These applications can often be cast into standard graph …

Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven
data. Specifically in signed and weighted graph structures. This makes classification tasks …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …