Enabling scalable social group analytics via hypergraph analysis systems

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, social interaction takes place not just between pairs of individuals as in the common graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through hypergraphs: a generalization of graphs. There are not yet, however, scalable systems to support hypergraph computation, and several challenges and opportunities arise in their design and implementation. In this paper, we present an initial attempt at building a scalable hypergraph analysis framework based on the GraphX/Spark framework. We use this prototype to examine several programmability and implementation issues through experiments with two real-world datasets on a 6-node cluster.