Fast Subgraph Isomorphism Detection for Graph-Based Retrieval
Markus Weber, Christoph Langenhan, Thomas Roth-Berghofer, Marcus Liwicki, Andreas Dengel, Frank Petzold
Case-Based Reasoning Research and Development Case-Based Reasoning Research and Development. International Conference on Case-Based Reasoning (ICCBR-11), September 12-15, Greenwich, London, United Kingdom

Abstract:

In this paper we present a method for a graph-based retrieval and its application in architectural floor plan retrieval. The proposed method is an extension of a well-known method for subgraph matching. This extension significantly reduces the storage amount and indexing time for graphs where the nodes are labeled with a rather small amount of different classes. In order to reduce the number of possible permutations, a weight function for labeled graphs is introduced and a well-founded total order is defined on the weights of the labels. Inversions which violate the order are not allowed. A computational complexity analysis of the new preprocessing is given and its completeness is proven. Furthermore, in a number of practical experiments with randomly generated graphs the improvement of the new approach is shown. In experiments performed on random sample graphs, the number of permutations has been decreased to a fraction of 10^−18 in average compared to the original approach by Messmer. This makes indexing of larger graphs feasible, allowing for fast detection of subgraphs.

Fast Subgraph Isomorphism Detection for Graph-Based Retrieval
Markus Weber, Christoph Langenhan, Thomas Roth-Berghofer, Marcus Liwicki, Andreas Dengel, Frank Petzold
Case-Based Reasoning Research and Development Case-Based Reasoning Research and Development. International Conference on Case-Based Reasoning (ICCBR-11), September 12-15, Greenwich, London, United Kingdom

Abstract:

In this paper we present a method for a graph-based retrieval and its application in architectural floor plan retrieval. The proposed method is an extension of a well-known method for subgraph matching. This extension significantly reduces the storage amount and indexing time for graphs where the nodes are labeled with a rather small amount of different classes. In order to reduce the number of possible permutations, a weight function for labeled graphs is introduced and a well-founded total order is defined on the weights of the labels. Inversions which violate the order are not allowed. A computational complexity analysis of the new preprocessing is given and its completeness is proven. Furthermore, in a number of practical experiments with randomly generated graphs the improvement of the new approach is shown. In experiments performed on random sample graphs, the number of permutations has been decreased to a fraction of 10^−18 in average compared to the original approach by Messmer. This makes indexing of larger graphs feasible, allowing for fast detection of subgraphs.