Recently, many applications have required the ability to perform dynamic graph analytical
processing (GAP) tasks on the datasets generated by relational OLTP in real time. To meet …

Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching
for small patterns of interest. GPM applications are computationally expensive, and thus …

Graph mining is an emerging application of high importance and also with high complexity,
thus requiring efficient hardware acceleration. Current accelerator designs only utilize …

Subgraph matching is one of the most important problems in graph analytics. Many
algorithms and systems have been proposed for subgraph matching. Most of these works …

Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …

Graph computing has become increasingly crucial in processing large-scale graph data,
with numerous systems developed for this purpose. Two years ago, we introduced …

Graph pattern mining is an essential task in many fields, which explores all the instances of
user-interested patterns in a data graph. Pattern-centric mining systems transform the …

Graph mining is an emerging application of great importance to big data analytic. Graph
mining algorithms are bottle-necked by both computation complexity and memory access …

Graph engines play a crucial role in modern data analytics pipelines, serving as a
middleware for handling complex queries across various domains, such as financial fraud …

Graph mining applications analyze the structural properties of large graphs. These
applications are computationally expensive because finding structural patterns requires …