Foundations of algorithms
ImageMap: An Image Indexing Method Based on Spatial Similarity
IEEE Transactions on Knowledge and Data Engineering
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trading uninitialized space for time
Information Processing Letters
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The need to perform isomorphism testing is emerging recently in many application domains such as graph-based data mining for discovering frequent common patterns in a graph database. Due to the complex nature of graph representations, the isomorphism testing between labeled graphs is one of the most time-consuming phases during the mining process. The canonical form of a graph that serves as the isomorphism certificate needs O(n!) to produce for a graph of order n, or Θ(Πi=1c(|πi|!)) if vertex invariants are employed to divide n vertices into c equivalence classes with |πi| vertices in each class i. In this paper, we propose a new algorithm to perform isomorphism testing of labeled graphs with worst case time complexity O(Σi=1c(|πi|!)), in which the product of all |πi|! terms is replaced by the sum of the terms and the asymptotic notation is changed from big theta to big oh. To the best of our knowledge, this proposed model is the latest work that focuses on the dealing of the isomorphism testing of labeled graphs. The result of this algorithm is directly applicable to the fields of graph isomorphism testing for labeled graphs.