Incremental graph pattern matching

  • Authors:

  • Wenfei Fan;Jianzhong Li;Jizhou Luo;Zijing Tan;Xin Wang;Yinghui Wu

  • Affiliations:

  • University of Edinburgh, Edinburgh, United Kingdom;Harbin Institute of Technologies, Harbin, China;Harbin Institute of Technologies, Harbin, China;Fudan University, Shanghai, China;University of Edinburgh, Edinburgh, United Kingdom;University of Edinburgh, Edinburgh, United Kingdom

  • Venue:
  • Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2011

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Abstract

Graph pattern matching has become a routine process in emerging applications such as social networks. In practice a data graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches from scratch via batch algorithms when the graph is updated. With this comes the need for incremental algorithms that compute changes to the matches in response to updates, to minimize unnecessary recomputation. This paper investigates incremental algorithms for graph pattern matching defined in terms of graph simulation, bounded simulation and subgraph isomorphism. (1) For simulation, we provide incremental algorithms for unit updates and certain graph patterns. These algorithms are optimal: in linear time in the size of the changes in the input and output, which characterizes the cost that is inherent to the problem itself. For general patterns we show that the incremental matching problem is unbounded, i.e., its cost is not determined by the size of the changes alone. (2) For bounded simulation, we show that the problem is unbounded even for unit updates and path patterns. (3) For subgraph isomorphism, we show that the problem is intractable and unbounded for unit updates and path patterns. (4) For multiple updates, we develop an incremental algorithm for each of simulation, bounded simulation and subgraph isomorphism. We experimentally verify that these incremental algorithms significantly outperform their batch counterparts in response to small changes, using real-life data and synthetic data.