Mining graph evolution rules

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

Volumn 5781 LNAI, Issue PART 1, 2009, Pages 115-130

  Berlingerio, Michele ^a   Bonchi, Francesco ^b   Bringmann, Björn ^c   Gionis, Aristides ^b  

^a ISTI CNR   (Italy)

^b YAHOO RESEARCH   (Spain)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

BIBLIOGRAPHIC DATA; COAUTHORSHIP; EVOLVING GRAPHS; FREQUENT PATTERNS; LARGE NETWORKS; LOCAL PATTERNS; MINIMUM SUPPORT THRESHOLDS; REAL-WORLD NETWORKS; SOCIAL NETWORKS; SUPPORT AND CONFIDENCE; TIME GRANULARITIES;

ASSOCIATIVE PROCESSING; IMAGE PROCESSING; MINING;

ROBOT LEARNING;

EID: 70350635471     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04180-8_25     Document Type: Conference Paper

References (23)

1. Aggarwal, C.C., Yu, P.S.: Online analysis of community evolution in data streams. In: SDM (2005)
2. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)
3. Borgwardt, K.M., Kriegel, H.-P., Wackersreuther, P.: Pattern mining in frequent dynamic subgraphs. In: ICDM (2006)
4. Bringmann, B., Nijssen, S.: What is frequent in a single graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858-863. Springer, Heidelberg (2008)
5. Calders, T., Ramon, J., Van Dyck, D.: Anti-monotonic overlap-graph support measures. In: ICDM (2008)
6. Desikan, P., Srivastava, J.: Mining temporally changing web usage graphs. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds.) WebKDD 2004. LNCS (LNAI), vol. 3932, pp. 1-17. Springer, Heidelberg (2006)
7. Ferlez, J., Faloutsos, C., Leskovec, J., Mladenic, D., Grobelnik, M.: Monitoring network evolution using MDL. In: ICDE (2008)
8. Fiedler, M., Borgelt, C.: Subgraph support in a single graph. In: Workshop on Mining Graphs and Complex Data, MGCS (2007)
9. Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58(301), 13-30 (1963)
10. Holder, L.B., Cook, D.J., Djoko, S.: Substucture discovery in the SUBDUE system. In: AAAI KDD Workshop (1994)
11. Inokuchi, A., Washio, T.: A fast method to mine frequent subsequences from graph sequence data. In: ICDM (2008)
12. Knuth, D.E.: The sandwich theorem. Electronic Journal of Combinatorics 1, 1 (1994)
13. Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: ICDM (2001)
14. Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Mining and Knowledge Discovery 11(3), 243-271 (2005)
15. Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: KDD (2008)
16. Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: KDD (2005)
17. Liu, Z., Yu, J.X., Ke, Y., Lin, X., Chen, L.: Spotting significant changing subgraphs in evolving graphs. In: ICDM (2008)
18. Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: Graphscope: parameter-free mining of large time-evolving graphs. In: KDD (2007)
19. Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: KDD (2006)
20. Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: KDD (2007)
21. Vanetik, N., Shimony, S.E., Gudes, E.: Support measures for graph data. Data Mining Knowledge Discovery 13(2), 243-260 (2006)
22. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: ICDM (2002)
23. Zhu, F., Yan, X., Han, J., Yu, P.S.: gPrune: A constraint pushing framework for graph pattern mining. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 388-400. Springer, Heidelberg (2007)