Sliding window-based approximate triangle counting with bounded memory usage

Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. However, approximately counting triangles in real-world streaming graphs with duplicate edges and sliding window model remains an unsolved problem. In this paper, we propose SWTC algorithm to address approximate sliding-window triangle counting problem in streaming graphs. In SWTC, we propose a fixed-length slicing strategy that addresses both sample maintaining and cardinality estimation issues with a bounded memory usage. We theoretically prove the superiority of our method in sample graph size and estimation accuracy under given memory upper bound. To further improve the performance of our algorithm, we propose two optimization techniques, vision counting to avoid computation peaks, and asynchronous grouping to stabilize the accuracy. Extensive experiments also confirm that our approach has higher accuracy compared with the baseline method under the same memory usage.