Efficient Streaming Algorithms for Graphlet Sampling Marco Bressan Cispa Helmholtz Center for Information Security Department of Computer Science Saarland University

Given a graph G and a positive integer k, the Graphlet Sampling problem asks to sample a connected induced k-vertex subgraph of G uniformly at random. Graphlet sampling enhances machine learning applications by transforming graph structures into feature vectors for tasks such as graph classification and subgraph identification, boosting neural network performance, and supporting clustered federated learning by capturing local structures and relationships.