Graph mining, which finds specific patterns in the graph, is becoming increasingly important
in various domains. We point out that accelerating graph mining suffers from the following …

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world
applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a …

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance
of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data …

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 a fundamental problem in graph analysis. In recent years, many
subgraph matching algorithms have been proposed, making it pressing and challenging to …

Graph pattern matching searches a data graph for all instances of one or more query
patterns. Since it is one of the most fundamental problems in graph analytics, many graph …

Graph pattern mining is compute-intensive in processing massive amounts of graph-
structured data. This paper presents Arya, an ultra-fast approximate graph pattern miner that …

Graph mining is of critical use in a number of fields such as social networks, knowledge
graphs, and fraud detection. As an NP-complete problem, accelerating computation …

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