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 …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …

Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Anomaly detection is an important problem with multiple applications, and thus has been
studied for decades in various research domains. In the past decade there has been a …

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an
important problem for intelligent transportation systems and sustainability. However, it is …

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-
cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes …

Detecting botnets in a network is crucial because bots impact numerous areas such as cyber
security, finance, health care, law enforcement, and more. Botnets are becoming more …