Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

With the development of e-commerce, fraud behaviors have been becoming one of the
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …

As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Graph anomaly detection, here, aims to find rare patterns that are significantly different from
other nodes. Attributed graphs containing complex structure and attribute information are …

This paper studies the problem of graph-level clustering, which is a novel yet challenging
task. This problem is critical in a variety of real-world applications such as protein clustering …