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 …

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 …

Summarizing graphs is of paramount importance due to diverse applications of large-scale
graph analysis. A popular family of summarization methods is the group-based approach …

We present new algorithms for graph summarization where the loss in utility is fully
controllable by the user. Specifically, we make three key contributions. First, we present a …

Objectives: While big data offers enormous potential for improving healthcare delivery, many
of the existing claims concerning big data in healthcare are based on anecdotal reports and …

We introduce a general notion of maturity in social networks that is based on the number of
triangles between groups/communities. In order to protect individual privacy upon possible …

Which one is better between two representative graph summarization models with and
without edge weights? From web graphs to online social networks, large graphs are …

Reaching consensus among multiple processes in a distributed system is fundamental to
coordinating distributed actions. We present a new approach to building survivable …

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party
computation. The basic idea is to compute the intersection of input sets without leaking …

Security companies have recently realised that mining massive amounts of security data can
help generate actionable intelligence and improve their understanding of Internet attacks. In …