Graph Compression with Stars

  • Faming Li
  • Zhaonian ZouEmail author
  • Jianzhong Li
  • Yingshu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


Making massive graph data easily understandable by people is a demanding task in a variety of real applications. Graph compression is an effective approach to reducing the size of graph data as well as its complexity in structures. This paper proposes a simple yet effective graph compression method called the star-based graph compression. This method compresses a graph by shrinking a collection of disjoint subgraphs called stars. Compressing a graph into the optimal star-based compressed graph with the highest compression ratio is shown to be NP-complete. We propose a greedy compression algorithm called StarZip. We experimentally verify that StarZip achieves compression ratios of 3.8–45.7 and 2.9–241.6 in terms of vertex count and edge count, respectively. Besides, we study the shortest path queries on compressed graphs. On the real graphs, the StarSSSP algorithm for processing shortest path queries on compressed graphs is 4X–20X faster than Dijkstra’s algorithm running on original graphs. The average absolute error between the query results of StarSSSP and the exact shortest distances is about 1. On the synthetic graphs, StarSSSP is up to 313X faster than Dijkstra’s algorithm, and the average absolute error is also about 1.


Graph compression Star Shortest path 



This work was partially supported by the National Natural Science Foundation of China (No. 61532015, No. 61672189, No. 61732003 and No. 61872106) and the National Science Foundation of USA (No. 1741277 and No. 1829674).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Faming Li
    • 1
  • Zhaonian Zou
    • 1
    Email author
  • Jianzhong Li
    • 1
  • Yingshu Li
    • 2
  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.Georgia State UniversityAtlantaUSA

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