2018/02/12 by Kijung Shin, Shin, Kijung, Euiwoong Lee +7
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Caching and Content Delivery #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #H.2.8 #Parallel #Social and Information Networks (cs.SI) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1802.04249
openalex publication_date 2018/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Given a graph stream, how can we estimate the number of triangles in it using multiple machines with limited storage? Specifically, how should edges be processed and sampled across the machines for rapid and accurate estimation? The count of triangles (i.e., cliques of size three) has proven useful in numerous applications, including anomaly detection, community detection, and link recommendation. For triangle counting in large and dynamic graphs, recent work has focused largely on streaming algorithms and distributed algorithms but little on their combinations for "the best of both worlds". In this work, we propose CoCoS, a fast and accurate distributed streaming algorithm for estimating the counts of global triangles (i.e., all triangles) and local triangles incident to each node. Making one pass over the input stream, COCOS carefully processes and stores the edges across multiple machines so that the redundant use of computational and storage resources is minimized. Compared to baselines, CoCoS is (a) Accurate: giving up to 39X smaller estimation error, (b) Fast: up to 10.4X faster, scaling linearly with the size of the input stream, and (c) Theoretically sound: yielding unbiased estimates.