Heintz, BenjaminSingh, ShivangiTesdahl, CoreyChandra, Abhishek2020-09-022020-09-022016-12-05https://hdl.handle.net/11299/216002With 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 just 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 that enables different design choices for the key implementation issues of hypergraph representation and partitioning.  We implement MESH on top of the popular GraphX graph processing framework in Apache Spark.  Using a variety of real datasets, we experimentally demonstrate that MESH provides flexibility based on data and application characteristics, and is competitive in performance to HyperX, another hypergraph processing system based on Spark, showing that simplicity and flexibility need not come at the cost of performance.en-USReport