Graph data is found in numerous domains such as for the analysis of social networks,
sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to …

Large graphs are often used to simulate and model complex systems in various research
and application fields. Because of its importance, frequent subgraph mining (FSM) in single …

There are two types of high-performance graph processing engines: low-and high-level
engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures …

Distributed data processing platforms such as MapReduce and Pregel have substantially
simplified the design and deployment of certain classes of distributed graph analytics …

Graph mining is an important category of graph algorithms that aim to discover structural
patterns such as cliques and motifs in a graph. While a great deal of work has been done …

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 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 …

In this paper we propose Fractal, a high performance and high productivity system for
supporting distributed graph pattern mining (GPM) applications. Fractal employs a dynamic …

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 …

Graph mining algorithms that aim at identifying structural patterns of graphs are typically
more complex than graph computation algorithms such as breadth first search. Researchers …