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 …

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 …

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 …

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 …

Transactional frequent subgraph mining identifies frequent structural patterns in a collection
of graphs. This research problem has wide applicability and increasingly requires higher …

Frequent subgraph mining (FSM) has important applications in areas such as bioinformatics,
social networks and others. In this paper, we present a highly scalable approach called …

Subgraph enumeration is a heavy-computing procedure that lies at the core of Graph
Pattern Mining (GPM) algorithms, whose goal is to extract subgraphs from larger graphs …

Graph mining has practical applications in many areas such as molecular substructure
explorer, web link analysis, fraud detection, outlier detection, chemical molecules, and social …

Knowledge mining from graph data has attracted many researchers over the past several
years. With the evolution of internet, computer technology, social networking sites, and web …