Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Organizations face a data discovery problem when their analysts spend more time looking
for relevant data than analyzing it. This problem has become commonplace in modern …

Many public knowledge bases are represented and stored as RDF graphs, where users can
issue structured queries on such graphs using SPARQL. With massive queries over large …

The specific characteristics of graph workloads make it hard to design a one-size-fits-all
graph storage system. Systems that support transactional updates use data structures with …

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were
constructed to store large-scale, real-world facts as (subject, predicate, object) triples-that …

Modern graphs exert colossal time and space pressure on graph analytics applications. In
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …

Dynamic graphs have been gaining increasing popularity across various application
domains. With the growing size of these graphs, the update performance as well as space …

Evolving graphs in the real world are large-scale and constantly changing, as hundreds of
thousands of updates may come every second. Monotonic algorithms such as Reachability …

Many big data applications in business and science require the management and analysis
of huge amounts of graph data. Suitable systems to manage and to analyze such graph data …