Approximate counting of cycles in streams

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6942 LNCS, Issue , 2011, Pages 677-688

  Manjunath, Madhusudan ^a   Mehlhorn, Kurt ^a   Panagiotou, Konstantinos ^a   Sun, He ^a,b  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^b FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE COUNTING; COUNTING PROBLEMS; DATA STREAM; FIXED SIZE; INPUT STREAMS; INSERTIONS AND DELETIONS; NON-TRIVIAL ALGORITHMS; SAMPLING ALGORITHM; SUBGRAPHS;

RANDOM VARIABLES;

ALGORITHMS;

EID: 80052819262     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23719-5_57     Document Type: Conference Paper

References (12)

1. Bar-Yossef, Z., Kumar, R., Sivakumar, D.: Reductions in streaming algorithms, with an application to counting triangles in graphs. In: Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 623-632 (2002)
2. Becchetti, L., Boldi, P., Castillo, C., Gionis, A.: Efficient semi-streaming algorithms for local triangle counting in massive graphs. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 16-24 (2008)
3. Bordino, I., Donato, D., Gionis, A., Leonardi, S.: Mining large networks with subgraph counting. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 737-742 (2008)
4. Buriol, L.S., Frahling, G., Leonardi, S., Marchetti-Spaccamela, A., Sohler, C.: Counting triangles in data streams. In: Proceedings of the 25th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 253-262 (2006)
5. Buriol, L.S., Frahling, G., Leonardi, S., Sohler, C.: Estimating clustering indexes in data streams. In: Arge, L., Hoffmann, M., Welzl, E. (eds.) ESA 2007. LNCS, vol. 4698, pp. 618-632. Springer, Heidelberg (2007)
6. Chien, S., Rasmussen, L.E., Sinclair, A.: Clifford algebras and approximating the permanent. Journal of Computer and System Sciences 67(2), 263-290 (2003)
7. Flum, J., Grohe, M.: The parameterized complexity of counting problems. SIAM Journal on Computing 33(4), 892-922 (2004)
8. Ganguly, S.: Estimating frequency moments of data streams using random linear combinations. In: Jansen, K., Khanna, S., Rolim, J.D.P., Ron, D. (eds.) RANDOM 2004 and APPROX 2004. LNCS, vol. 3122, pp. 369-380. Springer, Heidelberg (2004)
9. Jowhari, H., Ghodsi, M.: New streaming algorithms for counting triangles in graphs. In: Wang, L. (ed.) COCOON 2005. LNCS, vol. 3595, pp. 710-716. Springer, Heidelberg (2005)
10. Karmarkar, N., Karp, R., Lipton, R., Lovasz, L., Luby, M.: A Monte-Carlo algorithm for estimating the permanent. SICOMP: SIAM Journal on Computing 22, 284-293 (1993)
11. McGregor, A.: Open Problems in Data Streams and Related Topics. In: IITK Workshop on Algoriths For Data Sreams (2006), http://www.cse.iitk.ac.in/users/ sganguly/data-stream-probs.pdf
12. Muthukrishnan, S.: Data Streams: Algorithms and Applications. Foundations and Trends in Theoretical Computer Science 1(2) (2005)