Two - phase heuristics for the k- club problem

Two - phase heuristics for the k- club problem 1 Introduction</h5> During the last decade, the use of network models has been steadily growing in a large number of areas, e. g ., social network analysis, computational biology, and financial information data mining, [1–5] .</P>In all these areas it is important to identify groups of elements that form clusters. The characterization of a cluster may be made from several standpoints. In some cases, the most important aspect is the number (or the proportion) of direct links between pairs of elements. In other cases, the most important feature is the number of hops that separate pairs of elements, i. e ., the number of intermediate elements that are needed to establish a connection between any two members. The former case can be addressed with density-based network models such as cliques, quasi-cliques, k -cores and k -plexes [6–13] . The latter case can be addressed with diameter-based network models such as k -clubs.</P>Given an undirected graph G = ( V , E ) and a pair of nodes u , v ∈ V , the distance d i s t G ( u , v ) is the minimum number of edges needed to link u and v in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers & Operations Research Elsevier

Two - phase heuristics for the k- club problem

Loading next page...
 
/lp/elsevier/two-phase-heuristics-for-the-k-club-problem-BULEeL2gxy
Publisher
Elsevier
Copyright
Copyright © 2014 Elsevier Ltd
ISSN
0305-0548
eISSN
1873-765X
D.O.I.
10.1016/j.cor.2014.07.006
Publisher site
See Article on Publisher Site

Abstract

1 Introduction</h5> During the last decade, the use of network models has been steadily growing in a large number of areas, e. g ., social network analysis, computational biology, and financial information data mining, [1–5] .</P>In all these areas it is important to identify groups of elements that form clusters. The characterization of a cluster may be made from several standpoints. In some cases, the most important aspect is the number (or the proportion) of direct links between pairs of elements. In other cases, the most important feature is the number of hops that separate pairs of elements, i. e ., the number of intermediate elements that are needed to establish a connection between any two members. The former case can be addressed with density-based network models such as cliques, quasi-cliques, k -cores and k -plexes [6–13] . The latter case can be addressed with diameter-based network models such as k -clubs.</P>Given an undirected graph G = ( V , E ) and a pair of nodes u , v ∈ V , the distance d i s t G ( u , v ) is the minimum number of edges needed to link u and v in

Journal

Computers & Operations ResearchElsevier

Published: Dec 1, 2014

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

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off