I/O efficient ECC graph decomposition via graph reduction

I/O efficient ECC graph decomposition via graph reduction The problem of computing k-edge connected components (k- $$\mathsf {ECC}$$ ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of $$\mathsf {ECC}$$ ECC decomposition, which computes the k- $$\mathsf {ECC}$$ ECC s of a graph G for all possible k values. $$\mathsf {ECC}$$ ECC decomposition can be widely applied in a variety of applications such as graph-topology analysis, community detection, Steiner Component Search, and graph visualization. A straightforward solution for $$\mathsf {ECC}$$ ECC decomposition is to apply the existing k- $$\mathsf {ECC}$$ ECC computation algorithm to compute the k- $$\mathsf {ECC}$$ ECC s for all k values. However, this solution is not applicable to large graphs for two challenging reasons. First, all existing k- $$\mathsf {ECC}$$ ECC computation algorithms are highly memory intensive due to the complex data structures used in the algorithms. Second, the number of possible k values can be very large, resulting in a high computational cost when each k value is independently considered. In this paper, we address the above challenges, and study I/O efficient $$\mathsf {ECC}$$ ECC decomposition via graph reduction. We introduce two elegant graph reduction operators which aim to reduce the size of the graph loaded in memory while preserving the connectivity information of a certain set of edges to be computed for a specific k. We also propose three novel I/O efficient algorithms, $$\mathsf {Bottom}$$ Bottom - $$\mathsf {Up}$$ Up , $$\mathsf {Top}$$ Top - $$\mathsf {Down}$$ Down , and $$\mathsf {Hybrid}$$ Hybrid , that explore the k values in different orders to reduce the redundant computations between different k values. We analyze the I/O and memory costs for all proposed algorithms. In addition, we extend our algorithm to build an efficient index for Steiner Component Search. We show that our index can be used to perform Steiner Component Search in optimal I/Os when only the node information of the graph is allowed to be loaded in memory. In our experiments, we evaluate our algorithms using seven real large datasets with various graph properties, one of which contains 1.95 billion edges. The experimental results show that our proposed algorithms are scalable and efficient. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

I/O efficient ECC graph decomposition via graph reduction

Loading next page...
 
/lp/springer_journal/i-o-efficient-ecc-graph-decomposition-via-graph-reduction-fX9pdeXWxM
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-016-0451-4
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial