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 …

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while
minimizing the information loss? Large-scale graphs have become omnipresent, posing …

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 …

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …

Graph summarization is beneficial in a wide range of applications, such as visualization,
interactive and exploratory analysis, approximate query processing, reducing the on-disk …

Graphs, also known as networks, are an expressive data representation used in many
domains. Numerous algorithms have been designed to find interesting patterns in graphs …

Graphs are a popular data type found in many domains. Numerous techniques have been
proposed to find interesting patterns in graphs to help understand the data and support …

Fusing or aligning two or more networks is a fundamental building block of many graph
mining tasks (eg, recommendation systems, link prediction, collective analysis of networks) …

Rich availability of real world knowledge in a graph based on attributes of each vertex and
its interactions, is a valuable source of information. However, it is hard to derive this useful …