Real-Time PageRank on Dynamic Graphs

Modern data generation has grown to enormous proportions, with events occurring at increasingly higher rates. Yet for graph analytics, this growth in scale and velocity has not been matched by improved algorithm or infrastructure techniques: most systems still focus on post-mortem or static analysis. This paper builds on an efficient graph processing abstraction that enables online analysis of dynamically evolving graphs at scale. Integral to this abstraction is that events tied to both graph topology changes as well as algorithmic maintenance occur and are processed asynchronously, concurrently, and autonomously (i.e., without shared state).