# MESH: A Flexible Distributed Hypergraph Processing System

## TL;DR

MESH is a flexible, scalable distributed hypergraph processing system built on Spark that offers an easy-to-use API, demonstrating competitive performance and simplicity compared to existing solutions.

## Contribution

The paper introduces MESH, a novel hypergraph processing framework that extends existing graph systems with a flexible, scalable, and simpler implementation for multi-user group analysis.

## Key findings

- MESH achieves scalability with cluster size.
- MESH performs competitively with HyperX.
- MESH requires about 5X fewer lines of code.

## Abstract

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.