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 …

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 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 …

We consider the community search problem defined upon a large graph G: given a query
vertex q in G, to find as output all the densely connected subgraphs of G, each of which …

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 …

We consider extending decision support facilities toward large sophisticated networks, upon
which multidimensional attributes are associated with network entities, thereby forming the …

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 …

As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …

Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …

Quantum computing is a rapidly evolving field that enables exponential speed-up over
classical algorithms. At the heart of this revolutionary technology are quantum circuits, which …