Xiangyang Gou’s paper “Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges” has been accepted by SIGMOD 2021.

　　Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many graph applications. Due to the large volume and high dynamicity, it is both memory and time consuming to store and analyze streaming graphs accurately. It is a natural choice to resort to efficiently compute approximations for queries like triangle counting. Besides, the high demand of timeliness of many applications requires us to downgrade the importance of old items in the streaming graph, and delete them when appropriate. This expiration procedure is usually modeled as sliding windows. However, approximately counting triangles under sliding window model in real-world streaming graphs with edge duplications remain an unsolved problem. In this paper, we propose a method named Sliding Window Triangle Counting (SWTC). It uses a novel strategy to produce bounded-size unbiased sample over the sliding window to approximately counting triangles. Experiments confirm that it has higher accuracy than the baseline method which is a combination of former works.