  In modern data science problems, techniques for extracting value from big
data require performing large-scale optimization over heterogenous, irregularly
structured data. Much of this data is best represented as multi-relational
graphs, making vertex programming abstractions such as those of Pregel and
GraphLab ideal fits for modern large-scale data analysis. In this paper, we
describe a vertex-programming implementation of a popular consensus
optimization technique known as the alternating direction of multipliers
(ADMM). ADMM consensus optimization allows elegant solution of complex
objectives such as inference in rich probabilistic models. We also introduce a
novel hypergraph partitioning technique that improves over state-of-the-art
partitioning techniques for vertex programming and significantly reduces the
communication cost by reducing the number of replicated nodes up to an order of
magnitude. We implemented our algorithm in GraphLab and measure scaling
performance on a variety of realistic bipartite graph distributions and a large
synthetic voter-opinion analysis application. In our experiments, we are able
to achieve a 50% improvement in runtime over the current state-of-the-art
GraphLab partitioning scheme.

Artificial Intelligence;Distributed, Parallel, and Cluster Computing