Efficient Approximate Temporal Triangle Counting in Streaming with Predictions
This addresses the challenge of processing massive temporal graphs for applications like social network analysis, though it is incremental as it builds on existing approximate methods with predictions.
The authors tackled the problem of efficiently approximating temporal triangle counts in large-scale streaming temporal graphs, introducing STEP, which combines predictions with sampling to achieve scalability and accurate estimates for all eight temporal triangle types, demonstrating high-quality results and superior efficiency over state-of-the-art methods in experiments on graphs with up to billions of edges.
Triangle counting is a fundamental and widely studied problem on static graphs, and recently on temporal graphs, where edges carry information on the timings of the associated events. Streaming processing and resource efficiency are crucial requirements for counting triangles in modern massive temporal graphs, with millions of nodes and up to billions of temporal edges. However, current exact and approximate algorithms are unable to handle large-scale temporal graphs. To fill such a gap, we introduce STEP, a scalable and efficient algorithm to approximate temporal triangle counts from a stream of temporal edges. STEP combines predictions to the number of triangles a temporal edge is involved in, with a simple sampling strategy, leading to scalability, efficiency, and accurate approximation of all eight temporal triangle types simultaneously. We analytically prove that, by using a sublinear amount of memory, STEP obtains unbiased and very accurate estimates. In fact, even noisy predictions can significantly reduce the variance of STEP's estimates. Our extensive experiments on massive temporal graphs with up to billions of edges demonstrate that STEP outputs high-quality estimates and is more efficient than state-of-the-art methods.