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 …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

The acquisition, processing, mining, and visualization of sensory data for knowledge
discovery and decision support has recently been a popular area of research and …

The explosion in the amount of the available RDF data has lead to the need to explore,
query and understand such data sources. Due to the complex structure of RDF graphs and …

Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how
can we obtain and incrementally update a lossless summary of its current snapshot? As …

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 …

To help users get familiar with large RDF graphs, RDF summarization techniques can be
used. In this work, we study quotient summaries of RDF graphs, that is: graph summaries …

Are users of an online social network interested equally in all connections in the network? If
not, how can we obtain a summary of the network personalized to specific users? Can we …