Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …

With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …

Unsupervised multiplex graph representation learning (UMGRL) has received increasing
interest, but few works simultaneously focused on the common and private information …

Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …

We reveal a one-class homophily phenomenon, which is one prevalent property we find
empirically in real-world graph anomaly detection (GAD) datasets, ie, normal nodes tend to …

Sequential recommendation systems aim to predict the future behaviors of users based on
their historical interactions. Despite the success of neural architectures like Transformer and …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Anomaly detection is a crucial task in complex distributed systems. A thorough
understanding of the requirements and challenges of anomaly detection is pivotal to the …

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …