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 …

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 …

Pharmaceutical companies operate in a strictly regulated and highly risky environment in
which a single slip can lead to serious financial implications. Accordingly, the …

Semi-supervised learning seeks to learn a machine learning model when only a small
amount of the available data is labeled. The most widespread approach uses a graph prior …

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 …

Multilabel learning involving hundreds of thousands or even millions of labels is referred to
as extreme multilabel learning (XML), in which the labels often follow a power-law …

When dealing with tabular data, models based on decision trees are a popular choice due to
their high accuracy on these data types, their ease of application, and explainability …

Spatial-temporal graph neural networks (STGNNs) are promising in solving real-world
spatial-temporal forecasting problems. Recognizing the inherent sequential relationship of …