Abstract
We study the problem of how to calculate the importance score for each node in a graph where data are denoted as hash codes. Previous work has shown how to acquire scores in a directed graph. However, never has a scheme analyzed and managed the graph whose nodes consist of hash codes. We extend the past methods and design the undirected hash-based graph and rank algorithm. In addition, we present addition and deletion strategies on our graph and rank.
Firstly, we give a mathematical proof and ensure that our algorithm will converge for obtaining the ultimate scores. Secondly, we present our hash based rank algorithm. Moreover, the results of given examples illustrate the rationality of our proposed algorithm. Finally, we demonstrate how to manage our hash-based graph and rank so as to fast calculate new scores in the updated graph after adding and deleting nodes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baeza, P.B.: Querying graph databases. In: Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013, 22–27 June 2013, New York, NY, USA, pp. 175–188 (2013). https://doi.org/10.1145/2463664.2465216
Cai, H., Huang, Z., Srivastava, D., Zhang, Q.: Indexing evolving events from tweet streams. In: ICDE, pp. 1538–1539 (2016)
Cuzzocrea, A., Jiang, F., Leung, C.K.: Frequent subgraph mining from streams of linked graph structured data. In: Proceedings of the Workshops of the EDBT/ICDT 2015 Joint Conference (EDBT/ICDT), 27 March 2015, Brussels, Belgium, pp. 237–244 (2015). http://ceur-ws.org/Vol-1330/paper-37.pdf
Gao, S., Cheng, X., Wang, H., Chia, L.: Concept model-based unsupervised web image re-ranking. In: ICIP, pp. 793–796 (2009)
Ge, S.S., Zhang, Z., He, H.: Weighted graph model based sentence clustering and ranking for document summarization. In: ICIS, pp. 90–95 (2011)
Hua, Y., Jiang, H., Feng, D.: FAST: near real-time searchable data analytics for the cloud. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2014, 16–21 November 2014, New Orleans, LA, USA, pp. 754–765 (2014). https://doi.org/10.1109/SC.2014.67
Lei, Y., Li, W., Lu, Z., Zhao, M.: Alternating pointwise-pairwise learning for personalized item ranking. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2155–2158. ACM (2017)
Liu, Y., et al.: Deep self-taught hashing for image retrieval. IEEE Trans. Cybern. 49(6), 2229–2241 (2019)
Michaelis, S., Piatkowski, N., Stolpe, M. (eds.): Solving Large Scale Learning Tasks, Challenges and Algorithms - Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday. LNCS (LNAI), vol. 9580. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41706-6
Mihalcea, R.: Graph-based ranking algorithms for sentence extraction, applied to text summarization. Unt Sch. Works 170–173, 20 (2004)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)
Richter, F., Romberg, S., Hörster, E., Lienhart, R.: Multimodal ranking for image search on community databases. In: MIR, pp. 63–72 (2010)
Wang, Y., Zhu, L., Qian, X., Han, J.: Joint hypergraph learning for tag-based image retrieval. IEEE Trans. Image Process. PP(99), 1 (2018)
Yang, J., Jie, L., Hui, S., Kai, W., Rosin, P.L., Yang, M.H.: Dynamic match Kernel with deep convolutional features for image retrieval. IEEE Trans. Image Process. 27(11), 5288–5302 (2018)
Zhou, K., Liu, Y., Song, J., Yan, L., Zou, F., Shen, F.: Deep self-taught hashing for image retrieval. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM 2015, 26–30 October 2015, Brisbane, Australia, pp. 1215–1218 (2015). https://doi.org/10.1145/2733373.2806320
Acknowledgements
This work is supported by the Innovation Group Project of the National Natural Science Foundation of China No. 61821003 and the National Key Research and Development Program of China under grant No. 2016YFB0800402 and the National Natural Science Foundation of China No. 61672254. Thanks for Jay Chou, a celebrated Chinese singer whose songs have been accompanying the author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y. et al. (2019). Analysis and Management to Hash-Based Graph and Rank. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-26072-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26071-2
Online ISBN: 978-3-030-26072-9
eBook Packages: Computer ScienceComputer Science (R0)