LSketch: A label-enabled graph stream sketch toward time-sensitive queries

Yiling Zeng, Chuanfeng Jian, Chunyao Song, Tingjian Ge, Yuhan Li, Yuqing Zhou

Abstract: Heterogeneous graph streams represent data interactions in real-world applications and are characterized by dynamic and heterogeneous properties including varying node labels, edge labels and edge weights. The mining of graph streams is critical in fields such as network security, social network analysis, and traffic control. However, the sheer volume and high dynamics of graph streams pose significant challenges for efficient storage and accurate query analysis. To address these challenges, we propose LSketch, a novel sketch technique designed for heterogeneous graph streams. Unlike traditional methods, LSketch effectively preserves the diverse label information inherent in these streams, enhancing the expressive ability of sketches. Furthermore, as graph streams evolve over time, some edges may become outdated and lose their relevance. LSketch incorporates a sliding window model that eliminates expired edges, ensuring that the analysis remains focused on the most current and relevant data automatically. LSketch operates with sub-linear storage space and supports both structure-based and time-sensitive queries with high accuracy. We perform extensive experiments over four real datasets, demonstrating that LSketch outperforms state-of-the-art methods in terms of query accuracy and time efficiency.