In today's competitive and fast-evolving business environment, it is a critical time for
organizations to rethink how to make talent-related decisions in a quantitative manner …

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 …

Anomaly detection on attributed networks has attracted a surge of research attention due to
its broad applications in various high-impact domains, such as security, finance, and …

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …

Graph anomaly detection aims to identify the atypical substructures and has attracted an
increasing amount of research attention due to its profound impacts in a variety of …

Anomaly detection in dynamic networks aims to find network elements (eg, nodes, edges,
subgraphs, change points) with significantly different behaviors from the vast majority, it can …

Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise
to boost the organization, is extremely important for organizations as they are the most …

My research strives to develop fundamental graph-centric learning algorithms to reduce the
need for human supervision in low-resource scenarios. The focus is on achieving effective …

Subteam replacement is defined as finding the optimal candidate set of people who can best
function as an unavailable subset of members (ie, subteam) for certain reasons (eg, conflicts …

Anomalies are defined as rare observations that significantly deviate from the majority. In
recent years, with the networked data becoming ubiquitous, network anomaly detection …