Cited By
View all- Chae DKim BKim SKim S(2019)Incremental feature selection for efficient classification of dynamic graph bagsConcurrency and Computation: Practice and Experience10.1002/cpe.550232:18Online publication date: 12-Sep-2019
On Classifying Dynamic Graph Bags
Abstract
In this paper, we introduce a novel problem of dynamic graph bag classification, and propose a method to solve this problem. Here, a graph bag (simply, bag) corresponds to a training object that contains one or multiple graphs. Dynamic bag classification aims to build a classification model for bags which are presented in a dynamic fashion, i.e., emerging of new bags or graphs. Our proposed solution for this problem can gradually update the classification model whenever such changes are made to a bag dataset, rather than building a model from the scratch. We demonstrate the effectiveness of our proposed method by our extensive evaluation on a real-world graph dataset.
References
[1]
M. M. Adankon and M. Cheriet. Support vector machine. Encyclopedia of biometrics, pages 1504--1511, 2015.
[2]
R. Angelova and G. Weikum. Graph-based text classification: learn from your neighbors. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 485--492. ACM, 2006.
[3]
B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 983--990. IEEE, 2009.
[4]
T. Cootes, M. Ionita, C. Lindner, and P. Sauer. Robust and accurate shape model fitting using random forest regression voting. Computer Vision-ECCV 2012, pages 278--291, 2012.
[5]
M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering, 17(8):1036--1050, 2005.
[6]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10--18, 2009.
[7]
C. Helma, R. D. King, S. Kramer, and A. Srinivasan. The predictive toxicology challenge 2000--2001. Bioinformatics, 17(1):107--108, 2001.
[8]
K. Jahn and S. Kramer. Optimizing gspan for molecular datasets. In Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), pages 77--89, 2005.
[9]
C. Jiang, F. Coenen, and M. Zito. A survey of frequent subgraph mining algorithms. The Knowledge Engineering Review, 28(1):75--105, 2013.
[10]
A. Liaw, M. Wiener, et al. Classification and regression by randomforest. R news, 2(3):18--22, 2002.
[11]
S. Pan, X. Zhu, C. Zhang, and S. Y. Philip. Graph stream classification using labeled and unlabeled graphs. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 398--409. IEEE, 2013.
[12]
J. Tang, T. Wang, Q. Lu, J. Wang, and W. Li. A wikipedia based semantic graph model for topic tracking in blogosphere. AAAI Press/International Joint Conferences on Artificial Intelligence, 2011.
[13]
J. Wu, Z. Hong, S. Pan, X. Zhu, Z. Cai, and C. Zhang. Multi-graph-view subgraph mining for graph classification. Knowledge and Information Systems, 2016.
[14]
J. Wu, S. Pan, X. Zhu, and Z. Cai. Boosting for multi-graph classification. IEEE transactions on cybernetics, 45(3):416--429, 2015.
[15]
J. Wu, X. Zhu, C. Zhang, and S. Y. Philip. Bag constrained structure pattern mining for multi-graph classification. IEEE Transactions on Knowledge and Data Engineering, 26(10):2382--2396, 2014.
[16]
X. Yan and J. Han. gspan: Graph-based substructure pattern mining. In Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on, pages 721--724. IEEE, 2002.
Index Terms
- On Classifying Dynamic Graph Bags
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Published In
September 2017
324 pages
ISBN:9781450350273
DOI:10.1145/3129676
- Conference Chairs:
- Katarzyna Grygiel,
- Tomas Cerny,
- Program Chairs:
- Sung-Ryul Kim,
- Wei Wang
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New York, NY, United States
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Published: 20 September 2017
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RACS '17
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RACS '17: International Conference on Research in Adaptive and Convergent Systems
September 20 - 23, 2017
Krakow, Poland
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RACS '17 Paper Acceptance Rate 48 of 207 submissions, 23%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%
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View all- Chae DKim BKim SKim S(2019)Incremental feature selection for efficient classification of dynamic graph bagsConcurrency and Computation: Practice and Experience10.1002/cpe.550232:18Online publication date: 12-Sep-2019
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