Abstract
There is a wide diversity of applications for graph compression in web data management, scientific data processing, and social data analysis. In real-life applications like social media data processing, elements in a graph, typically vertices and edges, are arriving continuously. Compressing the graph before storing it in a database is important for real-time processing and analysis, while being a challenging yet interesting problem. A streaming lossless compression method, named as STT (streaming timeliness triangulation), is introduced in this paper. It is a time-efficient method for compressing a streaming graph, which differs itself from static graph compression methods in that: (1) it’s able to compress streaming graph without occupying extra storage; (2) it can achieve both low compression ratio and high throughput over the streaming graph; (3) it supports efficient graph query processing directly over compressed graphs. Thus, it can support a wide range of streaming graph processing tasks. Empirical study over a paper co-author graph and a real-life large-scale social network graph has shown the superiority of the newly proposed method over existing static graph compression methods.
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References
Adler, M., Mitzenmacher, M.: Towards compressing web graphs. In: Data Compression Conference, DCC 2001, Snowbird, Utah, USA, 27–29 March 2001, pp. 203–212 (2001)
Boldi, P., Vigna, S.: The webgraph framework I: compression techniques. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, New York, NY, USA, 17–20 May 2004, pp. 595–602 (2004)
Buehrer, G., Chellapilla, K.: A scalable pattern mining approach to web graph compression with communities. In: Proceedings of the International Conference on Web Search and Web Data Mining, WSDM 2008, Palo Alto, California, USA, 11–12 February 2008, pp. 95–106 (2008)
Chaturvedi, A., Acharjee, T.: An efficient modified common neighbor approach for link prediction in social networks. IOSR J. Comput. Eng. (IOSR-JCE) 12, 25–34 (2013)
Choromański, K., Matuszak, M., Mikisz, J.: Scale-free graph with preferential attachment and evolving internal vertex structure. J. Stat. Phys. 151(6), 1175–1183 (2013)
Cui, H.: Link prediction on evolving data using tensor-based common neighbor. In: 2012 Fifth International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 343–346. IEEE (2012)
Gilbert, A.C., Levchenko, K.: Compressing network graphs. In: Proceedings of the LinkKDD Workshop at the 10th ACM Conference on KDD (2004)
Kang, U., Faloutsos, C.: Beyond ‘caveman communities’: hubs and spokes for graph compression and mining. In: 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, 11–14 December 2011, pp. 300–309 (2011)
Latapy, M.: Main-memory triangle computations for very large (sparse (power-law)) graphs. Theor. Comput. Sci. 407(1), 458–473 (2008)
McGregor, A.: Graph mining on streams. In: Encyclopedia of Database Systems, pp. 1271–1275 (2009)
Smith, A.J.: CPU cache memories. In: SIGMETRICS, p. 219 (1989)
Acknowledgement
This work is partially supported by National Hightech R&D Program (863 Program) under grant number 2015AA015307, and National Science Foundation of China under grant number 61432006.
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Zhang, L., Gao, M., Qian, W., Zhou, A. (2016). Compressing Streaming Graph Data Based on Triangulation. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_15
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DOI: https://doi.org/10.1007/978-3-319-45835-9_15
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