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  1. Transactions on Knowledge Discovery from Data (TKDD)
  2. ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6
  3. Issue 4(Special Issue on the Best of SIGKDD 2011), December 2012
  4. Triangle listing in massive networks
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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6
Issue 4(Special Issue on the Best of SIGKDD 2011), December 2012
Special issue on best of SIGKDD 2011
Leakage in data mining: Formulation, detection, and avoidance
Summarizing data succinctly with the most informative itemsets
Triangle listing in massive networks
Multisource domain adaptation and its application to early detection of fatigue
Substantial improvements in the set-covering projection classifier CHIRP (composite hypercubes on iterated random projections)
Issue 3, October 2012
Issue 2, July 2012
Issue 1, March 2012
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2
ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

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Triangle listing in massive networks

Content Provider ACM Digital Library
Author Chu, Shumo Cheng, James
Copyright Year 2012
Abstract Triangle listing is one of the fundamental algorithmic problems whose solution has numerous applications especially in the analysis of complex networks, such as the computation of clustering coefficients, transitivity, triangular connectivity, trusses, etc. Existing algorithms for triangle listing are mainly in-memory algorithms, whose performance cannot scale with the massive volume of today's fast growing networks. When the input graph cannot fit in main memory, triangle listing requires random disk accesses that can incur prohibitively huge I/O cost. Some streaming, semistreaming, and sampling algorithms have been proposed but these are approximation algorithms. We propose an I/O-efficient algorithm for triangle listing. Our algorithm is exact and avoids random disk access. Our results show that our algorithm is scalable and outperforms the state-of-the-art in-memory and local triangle estimation algorithms.
Starting Page 1
Ending Page 32
Page Count 32
File Format PDF
ISSN 15564681
e-ISSN 1556472X
DOI 10.1145/2382577.2382581
Volume Number 6
Issue Number 4
Journal ACM Transactions on Knowledge Discovery from Data (TKDD)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-12-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Triangle listing Clustering coefficients Large graphs Massive networks Triangle counting
Content Type Text
Resource Type Article
Subject Computer Science
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