CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 49
  • Download : 0
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.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2021-04
Language
English
Article Type
Article
Citation

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v.15, no.3

ISSN
1556-4681
DOI
10.1145/3441487
URI
http://hdl.handle.net/10203/285392
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0