Provide an Optimal Model for Finding the Shortest Estimated Paths with Full Graph Coverage

Document Type : Persian Original Article

Authors

Department of Computer Engineering, Yazd University, Yazd, Iran.

Abstract

Due to the increasing volume of information in social networks and the web, the need for efficient and fast algorithms for analyzing graph content is felt more than ever. One of the most important operations in a graph is to find the shortest path between two nodes, which can have different applications in routing and communication. Classic algorithms are very slow and computationally expensive, nearly impossible, so algorithms using approximation approaches are often used based on Landmark nodes. In this study, four landmark models are introduced. Using innovative methods, landmark nodes are selected for each nodes cluster, the shortest paths are pre-computed and the results are Hashing for direct access. Hence, a fast, efficient and precise result retrieval is possible when an online query is executed. The proposed methods cover the entire graph can reduce the error rate by 0.0016.

Keywords


[1]    درودی، ا.، برادران هاشمی، ه.، آل احمد، ا.، زارع بیدکی، ع. م.، حبیبیان، ا. ح.، مهدیخانی، ف.، شاکری، آ.، و رهگذر، م. (۱۳۸۷). مجموعه محک استاندارد برای تحقیقات بازیابی اطلاعات وب فارسی. (شماره گزارش: DBRG-TR-138702). گروه تحقیقاتی پایگاه داده: دانشگاه تهران.
[2]          Madkour, A., Aref, W. G., Rehman, F. U., Rahman, M. A., & Basalamah, S. (2017). A survey of shortest-path algorithms. arXiv preprint arXiv:1705.02044.
[3]          Potamias, M., Bonchi, F., Castillo, C., & Gionis, A. (2009, November). Fast shortest path distance estimation in large networks. In Proceedings of the 18 th ACM conference on Information and knowledge management (pp. 867-876). ACM.
[4]          Goldberg, A. V., & Harrelson, C. (2005, January). Computing the shortest path: A search meets graph theory. In Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms (pp.156-165(. Society for Industrial and Applied Mathematics.
[5]          Goldberg, A. V. (2007, January). Point-to-point shortest path algorithms with preprocessing. In International Conference on Current Trends in Theory and Practice of Computer Science (pp. 88-102). Springer, Berlin, Heidelberg.
[6]          Grant, K., & Mould, D. (2008, July). LPI: Approximating shortest paths using landmarks. In Workshop on Artificial Intelligence in Games (p. 45).
[7]          Gubichev, A., Bedathur, S., Seufert, S., & Weikum, G. (2010). Fast and accurate estimation of shortest paths in large networks. In 19th ACM Conference on Information and Knowledge Management (pp. 499-508). ACM.‏
[8]          Cao, L., Zhao, X., Zheng, H., & Zhao, B. Y. (2011). Atlas: Approximating shortest paths in social graphs. Computer Science Department, U. C. Santa Barbara.
[9]          Qiao, M., Cheng, H., Chang, L., & Yu, J. X. (2012). Approximate shortest distance computing: A query-dependent local landmark scheme. IEEE Transactions on Knowledge and Data Engineering, 26(1), 55-68.
[10]        Floreskul, V., Tretyakov, K., & Dumas, M. (2014, May). Memory-efficient fast shortest path estimation in large social networks. In Eighth International AAAI Conference on Weblogs and Social Media.
[11]        Feng, C., & Deng, T. (2018, October). More Accurate Estimation of Shortest Paths in Social Networks. In 2018 International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) (pp. 314-317). IEEE.
[12]        Dong, Q., Lakhotia, K., Zeng, H., Karman, R., Prasanna, V., & Seetharaman, G. (2018, September). A Fast and Efficient Parallel Algorithm for Pruned Landmark Labeling. In 2018 IEEE High Performance extreme Computing Conference (HPEC)(pp.1-7) . IEEE.
[13]        J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. (2009). Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1) 29--123.