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 …

We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Prediction tasks over nodes and edges in networks require careful effort in engineering
features used by learning algorithms. Recent research in the broader field of representation …

We present DeepWalk, a novel approach for learning latent representations of vertices in a
network. These latent representations encode social relations in a continuous vector space …

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 …

Online social networks have received a dramatic increase of interest in the last decade due
to the growth of Internet and Web 2.0. They are among the most popular sites on the Internet …

In the last decade, the ease of online payment has opened up many new opportunities for e-
commerce, lowering the geographical boundaries for retail. While e-commerce is still …

Given a network, intuitively two nodes belong to the same role if they have similar structural
behavior. Roles should be automatically determined from the data, and could be, for …

Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …

Self-supervised space-time correspondence learning utilizing unlabeled videos holds great
potential in computer vision. Most existing methods rely on contrastive learning with mining …