Empowering Fast Incremental Computation over Large Scale Dynamic Graphs

Abstract

Unprecedented growth of online social networks, communication networks and internet of things have given birth to large volume, fast changing datasets. Data generated from such systems have an inherent graph structure in it. Updates in staggering frequencies (e.g. edges created by message exchanges in online social media) impose a fundamental requirement for real-time processing of unruly yet highly interconnected data. As a result, large-scale dynamic graph processing has become a new research frontier in computer science.In this paper, we present a new vertex-centric hierarchical bulk synchronous parallel model for distributed processing of dynamic graphs. Our model allows users to easily compose static graph algorithms similar to the widely used vertex-centric model. It also enables incremental processing of dynamic graphs by automatically executing user composed static graph algorithms in an incremental manner. We map widely used single source shortest path and connected component algorithms to this model and empirically analyze the performance on real-world large scale graphs. Experimental results show that our model improves the performance of both static and dynamic graph computation compared to the vertex-centric model by reducing the global synchronization overhead.

Incremental parallel computing for continuous queries in dynamic graphs using a transactional model

“…Its approach is based on the memoization of intermediate states and results. GraphInc and the hierarchical BSP model adopt the memoization‐based approach in the context of the BSP model of parallel programming supported by Pregel. The GraphIn framework takes a similar approach for incremental computation on any platform supporting GAS ( gather‐apply‐scatter ) model–based parallel programming.…”