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 …

Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

How can we describe a large, dynamic graph over time? Is it random? If not, what are the
most apparent deviations from randomness--a dense block of actors that persists over time …

How can we succinctly describe a million-node graph with a few simple sentences? How
can we measure the 'importance'of a set of discovered subgraphs in a large graph? These …

We study the problem of graph summarization. Given a large graph we aim at producing a
concise lossy representation (a summary) that can be stored in main memory and used to …

Networks are powerful instruments to study complex phenomena, but they become hard to
analyze in data that contain noise. Network backbones provide a tool to extract the latent …

Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …

Multilayer networks have been widely used to represent and analyze systems of
interconnected entities where both the entities and their connections can be of different …

In graph coarsening, one aims to produce a coarse graph of reduced size while preserving
important graph properties. However, as there is no consensus on which specific graph …