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 …

With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

A network is a typical expressive form of representing complex systems in terms of vertices
and links, in which the pattern of interactions amongst components of the network is intricate …

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 …

While there has been a tremendous interest in processing data that has an underlying graph
structure, existing distributed graph processing systems take several minutes or even hours …

Graph mining is becoming increasingly important due to the ever-increasing demands on
analyzing complex structures in graphs. Existing graph accelerators typically hold most of …

Recent advances in neuroscience have enabled the exploration of brain structure at the
level of individual synaptic connections. These connectomics datasets continue to grow in …

Graph mining is one of the most important categories of graph algorithms. However,
exploring the subgraphs of an input graph produces a huge amount of intermediate data …