We have discussed and built a real-world implementation of graph communities leveraging the power of a secure and robust architecture. We have outlined the idea that there is no right or wrong solution in the community detection problem space, as it strongly depends on the use case. In a social network context, for example, where vertices are tightly connected together (an edge represents a true connection between two users), the edge weight does not really matter while the triangle approach probably does. In the telecommunication industry, one could be interested in the communities based on the frequency call of a given user A to a user B, hence turning to a weighted algorithm such as Louvain.

We appreciate that building this community algorithm was far from an easy task, and perhaps stretches the goals of this book, but it involves all of the techniques of graph processing in Spark that makes GraphX a fascinating and extensible tool. We introduced the concepts of message passing, Pregel, graph partitioning, and variable broadcast, backed by a real-world implementation in Elasticsearch and Accumulo.

In the next chapter, we will apply the concepts of graph theory we learned here to the music industry, learning how to build a music recommendation engine using audio signal, Fourier transforms, and PageRank algorithm.