In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Knowledge graphs have, for the past decade, been a hot topic both in public and private
domains, typically used for large-scale integration and analysis of data using graph-based …

The future is big graphs: a community view on graph processing systems Page 1 62
COMMUNICATIONS OF THE ACM | SEPTEMBER 2021 | VOL. 64 | NO. 9 contributed articles …

Subgraph matching (or subgraph isomorphism) is one of the fundamental problems in graph
analysis. Extensive research has been done to develop practical solutions for subgraph …

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 …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

We report on a community effort between industry and academia to shape the future of
graph query languages. We argue that existing graph database management systems …

Both conceptual modeling and machine learning have long been recognized as important
areas of research. With the increasing emphasis on digitizing and processing large amounts …

Graph neural networks (GNNs) aim to learn a low-dimensional feature for each vertex in the
graph from its input high-dimensional feature, by aggregating the features of the vertex's …

Temporal property graphs are graphs whose structure and properties change over time.
Temporal graph datasets tend to be large due to stored historical information, asking for …