This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

The prevalence of graph structures in real-world scenarios enables important tasks such as
node classification and link prediction. Graphs in many domains follow a long-tailed …

Algorithmic bias and fairness in the context of graph mining have largely remained nascent.
The sparse literature on fair graph mining has almost exclusively focused on group-based …

Graph data widely exist in many high-impact applications. Inspired by the success of deep
learning in grid-structured data, graph neural network models have been proposed to learn …

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world
applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) …

Deep graph generative models have recently received a surge of attention due to its
superiority of modeling realistic graphs in a variety of domains, including biology, chemistry …

Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …

Learning representations for graphs plays a critical role in a wide spectrum of downstream
applications. In this paper, we summarize the limitations of the prior works in three folds …

High false-positive rate is a long-standing challenge for anomaly detection algorithms,
especially in high-stake applications. To identify the true anomalies, in practice, analysts or …