HIGGS: HIerarchy-Guided Graph Stream Summarization

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 domainbased, 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.

• We propose HIGGS, a novel item-based, bottom-up hierarchical structure designed for summarizing graph streams with temporal information. In addition, we design a expansion mechanism for compressed matrices, as well as data aggregation and query algorithms.

• This structure leverages its hierarchical organization to localize storage and query processing, effectively confining changes and hash conflicts to small, manageable subtrees. By integrating data aggregation and query algorithms, it achieves significant performance improvements.

• We provide a detailed theoretical analysis of our proposal, focusing on space and time efficiency, and most importantly, query accuracy, guaranteed by tighter theoretical bounds.

• We report on comprehensive and thorough experiments, finding that 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%,