Graph matching is a fundamental yet challenging problem in pattern recognition, data mining, and others. Graph matching aims to find node-to-node correspondence among multiple graphs, by solving an NP ...

Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks ...

The donors' graph is a graph which contains all the donors (the search space). It implemented using a LOL (List of Lists) representation written in cython for better time and memory efficiency. The ...

In order to study the application effect of graph pattern matching in medical field, Li et al. (2024) introduced the concept of probability graph pattern matching specially applicable to lung cancer ...

Graph matching remains a core challenge in computer vision, where establishing correspondences between features is crucial for tasks such as object recognition, 3D reconstruction and scene ...

Abstract Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph ...

Function-described graphs (FDG) have been introduced by the authors as a representation of an ensemble of attributed graphs (AG) for structural pattern recognition alternative to first-order random ...

Abstract The bipartite Star123 -free graphs were introduced by V. Lozin in [1] to generalize some already known classes of bipartite graphs. In this paper, we extend to bipartite Star123 -free graphs ...

Isomorphic pattern matching requires a double-shot function to ensure that the topology of the matching subgraph perfectly reflects the pattern graph. Typical algorithms include VF2 (Foggia et al., ...