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 …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

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 …

Nodes residing in different parts of a graph can have similar structural roles within their local
network topology. The identification of such roles provides key insight into the organization …

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 …

Embedding a web-scale information network into a low-dimensional vector space facilitates
tasks such as link prediction, classification, and visualization. Past research has addressed …

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 …