On computing the diameter of real-world undirected graphs

Theoretical Computer Science

Volumn 514, Issue , 2013, Pages 84-95

  Crescenzi, Pilu ^a   Grossi, Roberto ^b   Habib, Michel ^c   Lanzi, Leonardo ^a   Marino, Andrea ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b UNIVERSITY OF PISA   (Italy)

^c UNIVERSITÉ PARIS DIDEROT   (France)

Author keywords

Breadth first search; Complex network; Diameter; Experimental analysis

Indexed keywords

BREADTH-FIRST SEARCH; CLASSICAL PROBLEMS; DIAMETER; EXPERIMENTAL ANALYSIS; REAL-WORLD GRAPHS; UNDIRECTED GRAPH; UNWEIGHTED GRAPHS; WORST-CASE COMPLEXITY;

ALGORITHMS; COMPLEX NETWORKS;

GRAPH THEORY;

EID: 84889657803     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2012.09.018     Document Type: Article

References (41)

1. Z. Galil, and O. Margalit All pairs shortest distances for graphs with small integer length edges Information and Computation 134 2 1997 103 139
2. R. Seidel On the all-pairs-shortest-path problem in unweighted undirected graphs Journal of Computer and System Sciences 51 1995 400 403
3. U. Zwick Exact and Approximate Distances in Graphs - a survey F.M.a.d. Heide, ESA '01: Proceedings of the 9th Annual European Symposium on Algorithms 2001 Springer-Verlag London, UK 33 48
4. A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee Measurement and analysis of online social networks Proceedings of the 5th ACM/USENIX Internet Measurement Conference IMC'07 2007 ACM New York, NY, USA
5. SNAP, Stanford network analysis package (SNAP) Website, 2009. http://snap.stanford.edu.
6. P. Crescenzi, R. Grossi, C. Imbrenda, L. Lanzi, and A. Marino Finding the diameter in real-world graphs: experimentally turning a lower bound into an upper bound Proc. ESA LNCS vol. 6346 2010 302 313
7. D. Corneil, F. Dragan, M. Habib, and C. Paul Diameter determination on restricted graph families Discrete Applied Mathematics 113 2-3 2001 143 166
8. C. Magnien, M. Latapy, and M. Habib Fast computation of empirically tight bounds for the diameter of massive graphs Journal of Experimental Algorithmics 13 2008
9. P. Boldi, and S. Vigna The webgraph framework I: compression techniques Proceedings of the 13th International World Wide Web Conference 2003 ACM Press Manhattan, USA 595 601
10. J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani Kronecker graphs: an approach to modeling networks Journal of Machine Learning Research 11 2010 985 1042
11. J. Leskovec, J. Kleinberg, and C. Faloutsos Graph evolution: densification and shrinking diameters ACM Trans. Knowl. Discov. Data 1 1 2007
12. C.R. Palmer, P.B. Gibbons, C. Faloutsos, ANF: a fast and scalable tool for data mining in massive graphs, in: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 81-90.
13. R.G. on Graph Theory, D. o.t.U.P.d.C. U. Combinatorics, The degree diameter problem for general graphs, 2010, http://www-mat.upc.es/grup-de-grafs/.
14. C. Sommer, Christian Sommer's homepage, http://www.sommer.jp/graphs/, 2009.
15. U. Aron, Uri Alon lab, http://www.weizmann.ac.il/mcb/UriAlon/, 2002.
16. MetExplore, MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks, http://metexplore.toulouse.inra.fr/metexplore/, 2010.
17. J. Wu, T. Vallenius, K. Ovaska, J. Westermarck, T. Makela, and S. Hautaniemi Integrated network analysis platform for protein-protein interactions Nature Methods 6 2009 75 77
18. J. Duch, and A. Arenas Community identification using extremal optimization Physical Review E 72 2005 027104
19. HPRD, Human protein reference database, http://www.hprd.org/, 2003.
20. The jena protein-protein interaction website, http://ppi.fli-leibniz.de/, 2009.
21. iPfam, iPfam: the protein domain interactions database, http://ipfam.sanger.ac.uk/, 2009.
22. Integrated protein-protein interaction database of Synechocystis sp. PC 6803, http://bioportal.kobic.re.kr/SynechoNET/, 2007.
23. STRING, Functional protein association networks, http://string-db.org/, 2000.
24. A PPI graph from Intact DB, http://prabi2.inrialpes.fr/bamboo/, 2010.
25. CiteSeer, CiteSeer website, http://citeseer.ist.psu.edu/citeseer.html, 1997.
26. Cora, The Cora dataset, http://www.cs.umd.edu/~sen/lbc-proj/data/, 2007.
27. TrustLet, TrustLet website,://www.trustlet.org, 2007.
28. B.Y. Zhao, CURRENT LAB: social networking project, Data available, as explained at http://current.cs.ucsb.edu/facebook/, 2010.
29. Sandbox, Webscope from Yahoo! Labs, Data available, as explained at http://sandbox.yahoo.com/, 2010.
30. M. Latapy, FTP public directory, ftp://www-rp.lip6.fr/pub/latapy/ Measurement/, 2007.
31. P. Boldi, B. Codenotti, M. Santini, and S. Vigna UbiCrawler: a scalable fully distributed web crawler Software: Practice & Experience 34 8 2004 711 726
32. Clusters and communities, overlapping dense groups in networks, http://hal.elte.hu/cfinder/, 2005.
33. A. Stoica, Alina Stoica homepage, http://www.liafa.jussieu.fr/~stoica/.
34. Pajek dataset, http://vlado.fmf.uni-lj.si/pub/networks/data/default.htm, 2006.
35. C. Walshaw, The university of greenwich graph partitioning archive, http://staffweb.cms.gre.ac.uk/~c.walshaw/partition/, 2000.
36. P. Erdos, and A. Rényi On random graphs I Publicationes Mathematicae Debrecen 6 1959 290 297
37. M. Penrose Random Geometric Graphs Oxford Studies in Probability 2003
38. F.W. Takes, and W.A. Kosters Determining the diameter of small world networks Proceedings of the 20th ACM International Conference on Information and Knowledge Management CIKM'11 2011 ACM New York, NY, USA 1191 1196
39. L. Backstrom, P. Boldi, M. Rosa, J. Ugander, S. Vigna, Four degrees of separation, arXiv:1111.4570v1.
40. P. Crescenzi, R. Grossi, L. Lanzi, and A. Marino On computing the diameter of real-world directed (weighted) graphs SEA 2012 99 110
41. V. Chepoi, F. Dragan, B. Estellon, M. Habib, Y. Vaxès, Diameters, centers, and approximating trees of delta-hyperbolic geodesic spaces and graphs, in: Proc. SCG, 2008, pp. 59-68.