Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

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 …

Graph motifs are subgraph patterns that occur in complex networks, which are of key
importance for gaining deep insights into the structure and functionality of the graph. Motif …

In the era of big data, it is often the rare categories that are of great interest in many high-
impact applications, ranging from financial fraud detection in online transaction networks to …

Traditional methods for influential node identification usually require time consuming
network traversal to select the candidate node set. In this article we propose a new influence …

Rare categories abound in a number of real-world networks and play a pivotal role in a
variety of high-stakes applications, including financial fraud detection, network intrusion …

With the widespread development of algorithmic fairness, there has been a surge of
research interest that aims to generalize the fairness notions from the attributed data to the …

Graph classification has been widely used for knowledge discovery in numerous practical
application scenarios, such as social networks and protein-protein interaction networks …

We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB
is an effective tool for many real applications, such as content recommendation and online …