Big graphs

Abstract

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it comes to big graphs, these challenges become even more staggering. Each and every of the 4V's raises new questions, from theory to systems and practice. Is it possible to parallelize sequential graph algorithms and guarantee the correctness of the parallelized computations? Given a computational problem, does there exist a parallel algorithm for it that guarantees to reduce parallel runtime when more machines are used? Is there a systematic method for developing incremental algorithms with effectiveness guarantees in response to frequent updates? Is it possible to write queries across relational databases and semistructured graphs in SQL? Can we unify logic rules and machine learning, to improve the quality of graph-structured data, and deduce associations between entities? This paper aims to incite interest and curiosity in these topics. It raises as many questions as it answers.