Abstract
While network analysis is more than 70 years old, the analysis of paths in complex networks is yet almost negligible. Here, we introduce different measures of computing the pairwise similarity of paths, either simply based on the elements in the paths, their sequence, on the graph in which they are embedded, or incorporating all three features. Based on ground-truth in a data set concerning how people solve a one-player puzzle, we show that the classification of the paths using the similarity measures in a hierarchical clustering approach performs best for the similarity measures which integrate all three features. We thus give first evidence that path similarity measures provide another dimension to mine and analyze complex networks.
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References
Bashir, F., Khokhar, A., Schonfeld, D.: Segmented trajectory based indexing and retrieval of video data. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. II–623. IEEE (2003)
Buchin, K., Buchin, M., Gudmundsson, J., Löffler, M., Luo, J.: Detecting Commuting Patterns by Clustering Subtrajectories. In: Algorithms and Computation: 19th International Symposium, ISAAC 2008, Gold Coast, Australia, December 15-17, 2008. Proceedings, September, pp. 644–655 (2008)
Buzan, D., Sclaroff, S., Kollios, G.: Extraction and clustering of motion trajectories in video. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 521–524. IEEE (2004)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082–1090. ACM (2011)
Dorn, I., Lindenblatt, A., Zweig, K.A.: The trilemma of network analysis. In: Proceedings of the 2012 IEEE/ACM international conference on Advances in Social Network Analysis and Mining, Istanbul (2012)
González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)
Gudmundsson, J., Thom, A., Vahrenhold, J.: Of Motifs and Goals: Mining Trajectory Data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems - SIGSPATIAL ’12, pp. 129–138. ACM (2012)
Gusfield, D.: Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge University Press, New York, NY, USA (1997)
Jaccard, P.: Etude comparative de la distribution florale dans une portion des alpes et du jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Jarušek, P.: Modeling problem solving times in tutoring systems. Ph.D. thesis, Masarykova univerzita, Fakulta informatiky (2013)
Jarušek, P., Pelánek, R.: Analysis of a simple model of problem solving times. In: S. Cerri, W. Clancey, G. Papadourakis, K. Panourgia (eds.) Intelligent Tutoring Systems, Lecture Notes in Computer Science, vol. 7315, pp. 379–388. Springer, Berlin Heidelberg (2012)
Junejo, I.N., Javed, O., Shah, M.: Multi feature path modeling for video surveillance. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 716–719. IEEE (2004)
Kumar, P., Raju, B.S., Krishna, P.R.: A new similarity metric for sequential data. Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends: New Trends p. 233 (2011)
Laasonen, K.: Clustering and prediction of mobile user routes from cellular data. In: Knowledge Discovery in Databases: PKDD 2005, Lecture Notes in Computer Science, vol. 3721, pp. 569–576. Springer, Berlin Heidelberg (2005)
Makris, D., Ellis, T.: Path detection in video surveillance. Image and Vision Computing 20(12), 895–903 (2002)
Mannila, H., Moen, P.: Similarity between event types in sequences. In: Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, pp. 271–280. Springer, London (1999)
Mannila, H., Ronkainen, P.: Similarity of event sequences. In: Proceedings of the 4th International Workshop on Temporal Representation and Reasoning (TIME), p. 136. IEEE Computer Society (1997)
Moen, P.: Attribute, event sequence, and event type similarity notions for data mining. Ph.D. thesis, University of Helsinki, Department of Computer Science (2000)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering, pp. 673–684. IEEE (2002)
Wang, W., Zaïane, O.R.: Clustering web sessions by sequence alignment. In: Database and Expert Systems Applications, 2002. Proceedings. 13th International Workshop on, pp. 394–398. IEEE (2002)
Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)
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Bockholt, M., Zweig, K.A. (2017). Clustering of Paths in Complex Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_15
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DOI: https://doi.org/10.1007/978-3-319-50901-3_15
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