Abstract
As graph data emerging in various application e.g., biology, social network, scalable graph methods are required to analyze such data. However, traditional graph algorithms cannot meet the need due to their high complexity of both time and space. In this paper, a machine learning based method is proposed to predict shortest path under the case where complete accuracy is not required. A feed-forward neural network classification model and a regression model are constructed to deal with graphs with discrete path distance and continuous path distance, respectively. In addition, we further improve the above models to adapt the dynamic changes of graph data in the real world. The results on real-world datasets show that the proposed method can approach the shortest distance with lower error rate than the comparison methods. We also evaluated different graph embedding methods and training set construction methods on the experimental results.
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References
Thorup, M., Zwick, U.: Approximate distance oracles. J. ACM 52(1), 1–24 (2005)
Bonchi, F., Gionis, A., Gullo, F., Ukkonen, A.: Distance oracles in edge-labeled graphs. In: EDBT, pp. 547–558 (2014)
Guyon, I., et al.: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 (2017)
Zhao, X., Zheng, H.: Orion: Shortest path estimation for large social graphs. In: WOSN, pp. 1–9 (2010)
Zhao, X., Sala, A., Zheng, H., Zhao, B.Y.: Efficient shortest paths on massive social graphs. In: CollaborateCom, pp. 77–86. ICST / IEEE (2011)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)
Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39(1), 43–62 (1997)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, Second Edition. The MIT Press and McGraw-Hill Book Company (2001)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)
Fredman, M.L., Tarjan, R.E.: Fibonacci heaps and their uses in improved network optimization algorithms. J. ACM 34(3), 596–615 (1987)
Fredman, M.L., Willard, D.E.: Trans-dichotomous algorithms for minimum spanning trees and shortest paths. In: FOCS, pp. 719–725 (1990)
Chan, E.P.F., Yang, Y.: Shortest path tree computation in dynamic graphs. IEEE Trans. Comput. 58(4), 541–557 (2009)
D’Emidio, M., Forlizzi, L., Frigioni, D., Leucci, S., Proietti, G.: Hardness, approximability, and fixed-parameter tractability of the clustered shortest-path tree problem. J. Comb. Optim. 38(1), 165–184 (2019)
Pallottino, S., Scutellà, M.G.: Dual algorithms for the shortest path tree problem. Networks 29(2), 125–133 (1997)
Rizi, F.S., Schlötterer, J., Granitzer, M.: Shortest path distance approximation using deep learning techniques. In: ASONAM, pp. 1007–1014 (2018)
Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)
Potamias, M., Bonchi, F., Castillo, C., Gionis, A.: Fast shortest path distance estimation in large networks. In: CIKM, pp. 867–876 (2009)
Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 1–44 (2007)
Tu, C., Zhang, W., Liu, Z., Sun, M.: Max-margin DeepWalk: discriminative learning of network representation. In: IJCAI, pp. 3889–3895 (2016)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR (1), pp. 539–546 (2005)
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Qu, Z., Zong, Z., Zhang, J. (2023). Learning to Predict Shortest Path Distance. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_20
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DOI: https://doi.org/10.1007/978-3-031-46671-7_20
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