Finding Top-k Shortest Path Distance Changes in an Evolutionary Network

  • Manish Gupta
  • Charu C. Aggarwal
  • Jiawei Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)


Networks can be represented as evolutionary graphs in a variety of spatio-temporal applications. Changes in the nodes and edges over time may also result in corresponding changes in structural garph properties such as shortest path distances. In this paper, we study the problem of detecting the top-k most significant shortest-path distance changes between two snapshots of an evolving graph. While the problem is solvable with two applications of the all-pairs shortest path algorithm, such a solution would be extremely slow and impractical for very large graphs. This is because when a graph may contain millions of nodes, even the storage of distances between all node pairs can become inefficient in practice. Therefore, it is desirable to design algorithms which can directly determine the significant changes in shortest path distances, without materializing the distances in individual snapshots. We present algorithms that are up to two orders of magnitude faster than such a solution, while retaining comparable accuracy.


Short Path Source Node Active Node Node Pair Ranking Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manish Gupta
    • 1
  • Charu C. Aggarwal
    • 2
  • Jiawei Han
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.IBM T.J. Watson Research CenterUSA

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