Self-organizing map and tree topology for graph summarization

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

Volumn 7553 LNCS, Issue PART 2, 2012, Pages 363-370

  Doan, Nhat Quang ^a   Azzag, Hanane ^a   Lebbah, Mustapha ^a  

^a UNIVERSITÉ PARIS 13   (France)

Author keywords

graph summarization; hierarchical clustering; Self Organizing Map; tree topology

Indexed keywords

CLUSTERING METHODS; GRAPH SUMMARIZATION; GRAPH VERTEX; HIER-ARCHICAL CLUSTERING; NORMALIZED MUTUAL INFORMATION; REAL DATA SETS; TOPOLOGICAL MAP; TREE TOPOLOGY; TREE-STRUCTURE;

CONFORMAL MAPPING; DATA STRUCTURES; FORESTRY; MULTI AGENT SYSTEMS; NEURAL NETWORKS; TOPOLOGY;

TREES (MATHEMATICS);

ACCURACY; DATA PROCESSING; EXPERIMENTATION; GRAPHIC METHODS; HIERARCHICAL SYSTEMS; MAPPING; NEURAL NETWORKS;

EID: 84867673537     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33266-1_45     Document Type: Conference Paper

References (15)

1. Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: SIGMOD Conference, pp. 419-432 (2008)
2. Toivonen, H., Zhou, F., Hartikainen, A., Hinkka, A.: Compression of weighted graphs. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 965-973. ACM, New York (2011)
3. Azzag, H., Venturini, G., Oliver, A., Guinot, C.: A hierarchical ant based clustering algorithm and its use in three real-world applications. European Journal of Operational Research 179(3), 906-922 (2007)
4. Flake, G.W., Lawrence, S., Lee Giles, C.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 150-160. ACM, New York (2000)
5. Ino, H., Kudo, M., Nakamura, A.: Partitioning of web graphs by community topology. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 661-669. ACM, New York (2005)
6. Rossi, F., Villa-Vialaneix, N.: Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach. Neurocomput. 73, 1142-1163 (2010)
7. Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577-8582 (2006)
8. Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38 (June 2006)
9. Chakrabarti, D., Faloutsos, C., Zhan, Y.: Visualization of large networks with min-cut plots, a-plots and r-mat. Int. J. Hum.-Comput. Stud. 65(5), 434-445 (2007)
10. Chung, F.R.K.: Spectral Graph Theory (CBMS Regional Conference Series in Mathematics, No. 92). American Mathematical Society (February 1997)
11. Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17, 395-416 (2007)
12. Stanoev, A., Smilkov, D., Kocarev, L.: Identifying communities by influence dynamics in social networks. CoRR, abs/1104.5247 (2011)
13. Grygorash, O., Zhou, Y., Jorgensen, Z.: Minimum spanning tree based clustering algorithms. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006, pp. 73-81. IEEE Computer Society, Washington, DC (2006)
14. Strehl, A., Ghosh, J., Cardie, C.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583-617 (2002)
15. Auber, D.: Tulip: A huge graph visualisation framework. In: Mutzel, P., Jünger, M. (eds.) Graph Drawing Softwares. Mathematics and Visualization, pp. 105-126. Springer (2003)