SBG-Sketch: A Self-Balanced Sketch for Labeled-Graph Stream Summarization

TL;DR

SBG-Sketch is a novel self-balanced sketching technique for summarizing labeled-graph streams, effectively handling skewed label distributions and enabling efficient approximate graph queries with significantly reduced errors.

Contribution

Introduces SBG-Sketch, a self-balanced graph sketch that improves accuracy and efficiency in summarizing labeled-graph streams with skewed label distributions.

Findings

Reduces estimation errors by up to 99% compared to existing methods.

Handles highly skewed and dynamic label distributions effectively.

Supports efficient graph-traversal queries like reachability.

Abstract

Applications in various domains rely on processing graph streams, e.g., communication logs of a cloud-troubleshooting system, road-network traffic updates, and interactions on a social network. A labeled-graph stream refers to a sequence of streamed edges that form a labeled graph. Label-aware applications need to filter the graph stream before performing a graph operation. Due to the large volume and high velocity of these streams, it is often more practical to incrementally build a lossy-compressed version of the graph, and use this lossy version to approximately evaluate graph queries. Challenges arise when the queries are unknown in advance but are associated with filtering predicates based on edge labels. Surprisingly common, and especially challenging, are labeled-graph streams that have highly skewed label distributions that might also vary over time. This paper introduces Self-Balanced Graph Sketch (SBG-Sketch, for short), a graphical sketch for summarizing and querying labeled-graph streams that can cope with all these challenges. SBG-Sketch maintains synopsis for both the edge attributes (e.g., edge weight) as well as the topology of the streamed graph. SBG-Sketch allows efficient processing of graph-traversal queries, e.g., reachability queries. Experimental results over a variety of real graph streams show SBG-Sketch to reduce the estimation errors of state-of-the-art methods by up to 99%.