Computing subgraph frequencies is a fundamental task that lies at the core of several
network analysis methodologies, such as network motifs and graphlet-based metrics, which …

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

Counting the frequency of small subgraphs is a fundamental technique in network analysis
across various domains, most notably in bioinformatics and social networks. The special …

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 …

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 …

Counting the frequency of small subgraphs is a fundamental technique in network analysis
across various domains, most notably in bioinformatics and social networks. The special …

Clique counts reveal important properties about the structure of massive graphs, especially
social networks. The simple setting of just 3-cliques (triangles) has received much attention …

Clustering large data is a fundamental problem with a vast number of applications. Due to
the increasing size of data, practitioners interested in clustering have turned to distributed …

Clique counting is a fundamental task in network analysis, and even the simplest setting of 3-
cliques (triangles) has been the center of much recent research. Getting the count of k …

Big data machine learning and graph analytics have been widely used in industry,
academia and government. Continuous advance in this area is critical to business success …