HIGGS: HIerarchy-Guided Graph Stream Summarization

TL;DR

HIGGS introduces a hierarchical, item-based graph stream summarization method that significantly improves accuracy, reduces space, and enhances query efficiency compared to existing approaches.

Contribution

It proposes a novel bottom-up hierarchical structure for graph stream summarization, offering better accuracy, space efficiency, and query performance over prior methods.

Findings

Improves accuracy by over 3 orders of magnitude.

Reduces space overhead by an average of 30%.

Increases throughput by more than 5 times.

Abstract

Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topological and temporal information. Recent advancements, such as PGSS and Horae, address these limitations by using domain-based, top-down multi-layer structures in the form of compressed matrices. However, they either suffer from poor query accuracy, incur substantial space overheads, or have low query efficiency. This study proposes a novel item-based, bottom-up hierarchical structure, called HIGGS. Unlike existing approaches, HIGGS leverages its hierarchical structure to localize storage and query processing, thereby confining changes and hash conflicts to small and manageable subtrees, yielding notable performance improvements. HIGGS offers tighter theoretical bounds on query accuracy and space cost. Extensive empirical studies on real graph streams demonstrate that, compared to state-of-the-art methods, HIGGS is capable of notable performance enhancements: it can improve accuracy by over 3 orders of magnitude, reduce space overhead by an average of 30%, increase throughput by more than 5 times, and decrease query latency by nearly 2 orders of magnitude.