Graph matching algorithms for business process model similarity search

Abstract. We investigate the problem of ranking all process models in a repository according to their similarity with respect to a given process model. We focus specifically on the application of graph matching algorithms to this similarity search problem. Since the corresponding graph matching problem is NP-complete, we seek to find a compromise between computational complexity and quality of the computed ranking. Using a repository of 100 process models, we evaluate four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one. The results show that the mean average precision obtained by a fast greedy algorithm is close to that obtained with the most exhaustive algorithm.

A comparative study of ant colony optimization and reactive search for graph matching problems

Abstract. Many applications involve matching two graphs in order to identify their common features and compute their similarity. In this paper, we address the problem of computing a graph similarity measure based on a multivalent graph matching and which is generic in the sense that other well known graph similarity measures can be viewed as special cases of it. We propose and compare two different kinds of algorithms: an Ant Colony Optimization based algorithm and a Reactive Search. We compare the efficiency of these two algorithms on two different kinds of difficult graph matching problems and we show that they obtain complementary results.

An error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification

Graph edit distance is a powerful error-tolerant similarity measure for graphs. For pattern recognition problems involving large graphs, however, the high computational complexity makes it sometimes impossible to apply edit distance algorithms. In the present paper we propose an efficient algorithm for edit distance computation of planar graphs. Given graphs embedded in the plane, we iteratively match small subgraphs by locally optimizing structural correspondences. Eventually we obtain a valid edit path and hence an upper bound of the edit distance. To demonstrate the efficiency of our approach, we apply the proposed algorithm to the problem of fingerprint classification

Comparing Graph Similarity Measures for Graphical Recognition

In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique

Constraint-Based Graph Matching

Abstract. Measuring graph similarity is a key issue in many applications. We propose a new constraint-based modeling language for defining graph similarity measures by means of constraints. It covers measures based on univalent matchings, such that each node is matched with at most one node, as well as multivalent matchings, such that a node may be matched with a set of nodes. This language is designed on top of Comet, a programming language supporting both Constraint Programming (CP) and Constraint-Based Local Search (CBLS). Starting from the constraint-based description of the measure, we automatically generate a Comet program for computing the measure. Depending on the measure characteristics, this program either uses CP —which is better suited for computing exact measures such as (sub)graph isomorphism — or CBLS —which is better suited for computing error-tolerant measures such as graph edit distances. First experimental results show the feasibility of our approach.