3 papers
cs.DS2026
Single-Pass Estimation of the Clustering Coefficient Distribution in Graph Streams
Cristian Boldrin, C. Seshadhri
Triangle counting is one of the most fundamental problems in network analysis. Given the massive sizes of real-world graphs, there is a long history of small-space streaming algori…
cs.LG2026
Sensitivity Sampling with Predictions for k-Means Clustering
Cristian Boldrin, Fabio Vandin
We study the problem of k-means clustering on large datasets. The state-of-the-art for the problem is given by coresets-based approaches, which build small weighted summaries of th…
cs.DS2024
Fast and Accurate Triangle Counting in Graph Streams Using Predictions
Cristian Boldrin, Fabio Vandin
In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room…