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 …

Big graph mining is an important research area and it has attracted considerable attention. It
allows to process, analyze, and extract meaningful information from large amounts of graph …

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 …

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 …

Frequent Subgraph Mining is an essential operation for graph analytics and knowledge
extraction. Due to its high computational cost, parallel solutions are necessary. Existing …

Frequent subgraph mining (FSM) is an important task for exploratory data analysis on graph
data. Over the years, many algorithms have been proposed to solve this task. These …

We propose a novel distributed algorithm for mining frequent subgraphs from a single, very
large, labeled network. Our approach is the first distributed method to mine a massive input …

Mining frequent subgraphs from a large collection of graph objects is an important problem
in several application domains such as bio-informatics, social networks, computer vision …

The Rainbow Range Finder (RRF) has the ability to obtain range information for all image
pixels with only one frame TV camera imaging during 1/30 s. The authors propose a novel …