Abstract
Weighted frequent subgraph mining comes with an inherent challenge—namely, weighted support does not support the downward closure property, which is often used in mining algorithms for reducing the search space. Although this challenge attracted attention from several researchers, most existing works in this field use either affinity based pruning or alternative anti-monotonic weighting technique for subgraphs other than average edge-weight. In this paper, we propose an efficient weighted frequent subgraph mining algorithm called WFSM-MaxPWS. Our algorithm uses the MaxPWS pruning technique, which significantly reduces search space without changing subgraph weighting scheme while ensuring completeness. Our evaluation results on three different graph datasets with two different weight distributions (normal and negative exponential) showed that our WFSM-MaxPWS algorithm led to significant runtime improvement over the existing MaxW pruning technique (which is a concept for weighted pattern mining in computing subgraph weight by taking average of edge weights).
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References
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K., Choi, H.J.: Single-pass incremental and interactive mining for weighted frequent patterns. Expert Syst. Appl. 39(9), 7976–7994 (2012)
Babu, N., John, A.: A distributed approach to weighted frequent subgraph mining. In: ICETT 2016. IEEE (2016). https://doi.org/10.1109/ICETT.2016.7873705
Cheng, Z., Flouvat, F., Selmaoui-Folcher, N.: Mining recurrent patterns in a dynamic attributed graph. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 631–643. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57529-2_49
Eichinger, F., Huber, M., Böhm, K.: On the usefulness of weight-based constraints in frequent subgraph mining. In: Bramer, M., Petridis, M., Hopgood, A. (eds.) Research and Development in Intelligent Systems, vol. XXVII, pp. 65–78. Springer, London (2011). https://doi.org/10.1007/978-0-85729-130-1_5
Fariha, A., Ahmed, C.F., Leung, C.K.-S., Abdullah, S.M., Cao, L.: Mining frequent patterns from human interactions in meetings using directed acyclic graphs. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7818, pp. 38–49. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37453-1_4
Gupta, A., Thakur, H., Gupta, T., Yadav, S.: Regular pattern mining (with jitter) on weighted-directed dynamic graphs. JESTEC 12(2), 349–364 (2017)
Huang, Z., Ye, Y., Li, X., Liu, F., Chen, H.: Joint weighted nonnegative matrix factorization for mining attributed graphs. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 368–380. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_29
Jiang, C., Coenen, F., Zito, M.: Finding frequent subgraphs in longitudinal social network data using a weighted graph mining approach. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010. LNCS (LNAI), vol. 6440, pp. 405–416. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17316-5_39
Jiang, C., Coenen, F., Zito, M.: Frequent sub-graph mining on edge weighted graphs. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2010. LNCS, vol. 6263, pp. 77–88. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15105-7_7
Lee, G., Yun, U.: Mining strongly correlated sub-graph patterns by considering weight and support constraints. IJMUE 8(1), 197–206 (2013)
Lee, G., Yun, U., Kim, D.: A weight-based approach: frequent graph pattern mining with length-decreasing support constraints using weighted smallest valid extension. Adv. Sci. Lett. 22(9), 2480–2484 (2016)
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68125-0_61
Leung, C.K.-S., Tanbeer, S.K.: PUF-tree: a compact tree structure for frequent pattern mining of uncertain data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7818, pp. 13–25. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37453-1_2
Ozaki, T., Etoh, M.: Closed and maximal subgraph mining in internally and externally weighted graph databases. In: IEEE AINA 2011 Workshops, pp. 626–631 (2011)
Shinoda, M., Ozaki, T., Ohkawa, T.: Weighted frequent subgraph mining in weighted graph databases. In: IEEE ICDM 2009 Workshops, pp. 58–63 (2009)
Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: IEEE ICDM 2002, pp. 721–724 (2002)
Yang, J., Su, W., Li, S., Dalkilic, M.M.: WIGM: discovery of subgraph patterns in a large weighted graph. In: SIAM SDM 2012, pp. 1083–1094 (2012)
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This project is partially supported by NSERC (Canada) and U. Manitoba.
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Islam, M.A., Ahmed, C.F., Leung, C.K., Hoi, C.S.H. (2018). WFSM-MaxPWS: An Efficient Approach for Mining Weighted Frequent Subgraphs from Edge-Weighted Graph Databases. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_52
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