Compressing Streaming Graph Data Based on Triangulation

  • Liang ZhangEmail author
  • Ming Gao
  • Weining Qian
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9865)


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.


Graph compression Streaming data Social graph Graph query 



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Liang Zhang
    • 1
    Email author
  • Ming Gao
    • 1
  • Weining Qian
    • 1
  • Aoying Zhou
    • 1
  1. 1.Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina

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