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 …

In massive and rapid graph streams, a useful and important task is to summarize the
structure of graph streams in order to enable efficient and effective graph query processing …

Given a terabyte-scale graph distributed across multiple machines, how can we summarize
it, with much fewer nodes and edges, so that we can restore the original graph exactly or …

Given a large graph, how can we summarize it with fewer nodes and edges while
maintaining its key properties, eg node degrees and graph spectrum? As a solution, graph …

Graphs naturally represent information ranging from links between web pages, to
communication in email networks, to connections between neurons in our brains. These …

Given a set of temporal networks, from different domains and with different sizes, how can
we compare them? Can we identify evolutionary patterns that are both (i) characteristic and …

Temporal (or time-evolving) networks are commonly used to model complex systems and
the evolution of their components throughout time. Although these networks can be …

Complex Event Processing (CEP) is a paradigm to detect event patterns over streaming data
in a timely manner. Presently, CEP systems have inherent limitations to detect event patterns …

The phenomena of influence diffusion on social networks have received tremendous
research interests in the past decade. While most prior works mainly focus on predicting the …

In this paper, we propose a novel unsupervised graph representation approach in a graph
stream called SNAPSKETCH that can be used for anomaly detection. It first performs a fixed …