Graph Analytics on Evolving Data (Abstract)

TL;DR

CommonGraph is an innovative approach that efficiently processes queries on evolving graphs by converting deletions to additions, enabling parallelism and sharing of updates, resulting in significant performance improvements.

Contribution

It introduces CommonGraph, a novel method that transforms deletions into additions and enables parallel processing for evolving graph analytics.

Findings

Achieves 1.38x-8.17x performance improvement over Kickstarter.

Effectively handles graph updates by converting deletions to additions.

Enables parallel processing of graph snapshots for faster analytics.

Abstract

We consider the problem of graph analytics on evolving graphs. In this scenario, a query typically needs to be applied to different snapshots of the graph over an extended time window. We propose CommonGraph, an approach for efficient processing of queries on evolving graphs. We first observe that edge deletions are significantly more expensive than addition operations. CommonGraph converts all deletions to additions by finding a common graph that exists across all snapshots. After computing the query on this graph, to reach any snapshot, we simply need to add the missing edges and incrementally update the query results. CommonGraph also allows sharing of common additions among snapshots that require them, and breaks the sequential dependency inherent in the traditional streaming approach where snapshots are processed in sequence, enabling additional opportunities for parallelism. We incorporate the CommonGraph approach by extending the KickStarter streaming framework. CommonGraph achieves 1.38x-8.17x improvement in performance over Kickstarter across multiple benchmarks.