  With the rapid growth of large online social networks, the ability to analyze
large-scale social structure and behavior has become critically important, and
this has led to the development of several scalable graph processing systems.
In reality, however, social interaction takes place not only between pairs of
individuals as in the graph model, but rather in the context of multi-user
groups. Research has shown that such group dynamics can be better modeled
through a more general hypergraph model, resulting in the need to build
scalable hypergraph processing systems. In this paper, we present MESH, a
flexible distributed framework for scalable hypergraph processing. MESH
provides an easy-to-use and expressive application programming interface that
naturally extends the think like a vertex model common to many popular graph
processing systems. Our framework provides a flexible implementation based on
an underlying graph processing system, and enables different design choices for
the key implementation issues of partitioning a hypergraph representation. We
implement MESH on top of the popular GraphX graph processing framework in
Apache Spark. Using a variety of real datasets and experiments conducted on a
local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate
that MESH provides flexibility based on data and application characteristics,
as well as scalability with cluster size. We further show that it is
competitive in performance to HyperX, another hypergraph processing system
based on Spark, while providing a much simpler implementation (requiring about
5X fewer lines of code), thus showing that simplicity and flexibility need not
come at the cost of performance.
