Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Efficient Shortest Path Techniques on a Summarized Graph based on the Relationships

관계기반 요약그래프에서 효율적인 최단경로 탐색기법

  • 김현욱 (경희대학교 컴퓨터공학과) ;
  • 서호진 (경희대학교 컴퓨터공학과) ;
  • 이영구 (경희대학교 컴퓨터공학과)
  • Received : 2017.02.09
  • Accepted : 2017.04.12
  • Published : 2017.07.15
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Abstract

As graphs are becoming increasingly large, the costs for storing and managing data are increasing continuously. Shortest path discovery over a large graph requires long running time due to frequent disk I/Os and high complexity of the graph data. Recently, graph summarization techniques have been studied, which reduce the size of graph data and disk I/Os by representing highly dense subgraphs as a single super-node. Decompressing should be minimized for efficient shortest path discovery over the summarized graph. In this paper, we analyze the decompression performance of a summarized graph and propose an approximate technique that discovers the shortest path quickly with a minimum error ratio. We also propose an exact technique that efficiently discovered the shortest path by exploiting an index built on paths containing super-nodes. In our experiments, we showed that the proposed technique based on the summarized graph can reduce the running time by up to 70% compared with the existing techniques performed on the original graph.

그래프 데이터가 대용량화됨에 따라 데이터를 저장 및 유지하기 위한 비용이 지속적으로 증가하고 있다. 이와 같은 대용량 그래프에서 최단경로를 탐색하는 것은 빈번한 디스크 I/O와 그래프의 높은 복잡도로 인해 매우 오랜 수행시간을 요구한다. 최근 그래프의 밀집도가 높은 부분그래프를 하나의 슈퍼노드로 표현하여 그래프 크기와 디스크 I/O를 줄이는 그래프 요약 연구가 수행되고 있다. 이와 같은 요약된 그래프에서 효율적으로 최단경로를 탐색하기 위해서는 요약그래프의 복원을 최소화해야 한다. 본 논문에서는 요약그래프의 복원 성능을 분석하고, 이를 이용하여 오차를 최소화하며 빠르게 최단경로를 탐색하는 근사 기법을 제안한다. 또한 최단경로 탐색과정 중 복원이 요구되는 슈퍼노드가 포함된 경로를 사전에 색인으로 구축하여 정확한 최단경로를 효율적으로 탐색하는 기법을 제안한다. 실세계 데이터를 이용한 실험을 통하여 제안하는 요약그래프에서의 최단거리 탐색기법이 원본 그래프를 고려한 기법들보다 최대 70%로 수행시간이 향상되었음을 보인다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. Akiba, Takuya, Yoichi Iwata, and Yuichi Yoshida, "Fast exact shortest-path distance queries on large networks by pruned landmark labeling," Proc. of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 349-360, Jun. 2013.
  2. Lingkun Wu, Xiaokui Xiao, et al., "Shortest path and distance queries on road networks: An experimental evaluation," Proc. of the VLDB Endowment, Vol. 5, Issue. 5, pp. 406-417, Jan. 2012.
  3. Konstantin Tretyakov, Abel Armas-Cervantes, et al., "Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs," Proc. of the 20th ACM international conference on Information and knowledge management, pp. 1785-1794, Oct. 2011.
  4. Michael Rice and Vassilis J. Tsotras, "Graph indexing of road networks for shortest path queries with label restrictions," Proc. of the VLDB Endowment, Vol. 4, Issue. 2, pp. 69-80, Nov. 2010.
  5. Saket Navlakha, Rajeev Rastogi and Nisheeth Shrivastava, "Graph summarization with bounded error," Proc. of the 2008 ACM SIGMOD international conference on Management of data, pp. 419-432, Jun. 2008.
  6. Kifayat Ullah Khan, Waqas Nawaz and Young-Koo Lee, "Set-based approximate approach for lossless graph summarization," Computing, Vol. 97, Issue. 12, pp. 1185-1207, Dec. 2015. https://doi.org/10.1007/s00607-015-0454-9
  7. Danai Koutra, U Kang, Jilles Vreeken and Christos Faloutsos, "VoG: summarizing and understanding large graphs," Proc. of the 2014 SIAM international conference on data mining, pp. 91-99, Jun. 2014.
  8. HoJin Seo, Hyunwook Kim, Kisung Park, Yongkoo Han, Young-Koo Lee, "Summarization Technique on a Compressed Graph for Massive Graph Analysis," Korean Society for Big Data Service, Vol. 2, No. 1, pp. 25-35, 2015.
  9. K. Wu, E. J. Otoo, A. Shosahni, H. Nordberg, "Notes on Design and Implementation of Compressed Bit Vectors," Lawrence Berkeley National Laboratory, 2001.
  10. Large-scale real-world social graphs, http://snap.stanford.edu