Distributed processing of large-scale graph data has many practical applications and has
been widely studied. In recent years, a lot of distributed graph processing frameworks and …

Graph mining workloads aim to extract structural properties of a graph by exploring its
subgraph structures. General purpose graph mining systems provide a generic runtime to …

There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable
memory with higher density and lower cost than DRAM. This enables the design of …

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) is a key building block in diverse applications, including
bioinformatics, chemical engineering, social network analysis, recommender systems and …

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

Triangle counting is a building block for a wide range of graph applications. Traditional
wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle …

Triangle count and local clustering coefficient are two core metrics for graph analysis. They
find broad application in analyses such as community detection and link recommendation …

Understanding the higher-order interactions within network data is a key objective of
network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched …