Graph pattern matching is commonly used in a variety of emerging applications such as social network analysis. These applications highlight the need for studying the following two issues. First, graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong a topological constraint on graphs to identify meani...

We consider square matrices A that commute with a fixed square matrix K, both with entries in a field F not of characteristic 2. When K2 = I, Tao and Yasuda defined A to be generalized centrosymmetric with respect to K. When K2 = −I, we define A to be pseudo-centrosymmetric with respect to K; we show that the determinant of every even-order pseudo-centrosymmetric matrix is the sum of two square...

This chapter explains the graph matching problem in detail. We first introduce some notation and terminology. Next, a classification of the different graph matching types is presented: this PhD thesis concentrates on inexact graph matching problems, but this chapter summarizes other types of graph matching too. The complexity of the different graph matching problems is also analyzed. Finally, t...

Graphs are high expressive models and especially suitable for modelling highly complex structures, the number of applications that use them for modelling both their data and the processes that manipulate has grown dramatically in recent years. The use of new conceptual structures in real-world applications requires the development of new systems to store and query such structures in order to al...

In Pattern Recognition, the Graph Matching problem involves the matching of a sample graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present Rulegraphs, a new method that combines the Graph Matching approach with Rule-Based approaches from Machine Learning. They reduce the cardinality of the (NP-Complete...

Schema matching is the task of finding semantic correspondences between elements of two schemas, which plays a key role in many database applications. In this paper, we cast the schema matching problem (SMP) into a multi-labeled graph matching problem. First, we propose an internal schema model: multi-labeled graph model, and transform schemas into multi-labeled graphs. Therefore, SMP reduce to...

In a variety of emerging applications one needs to decide whethera graph G matches another Gp, i.e., whether G has a topologicalstructure similar to that of Gp. The traditional notions of graphhomomorphism and isomorphism often fall short of capturing thestructural similarity in these applications. This paper studies revisions of these notions, providing a full treatment from co...

This paper describes a Bayesian graph matching algorithm for data-mining from large structural databases. The matching algorithm uses edge-consistency and node attribute similarity to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node feature-vectors are constructed by computing normalised histograms of pairwise geometric attribut...