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 function matching finds matches up to 3 mismatches and return a pandas.DataFrame object of the matches sorted by number of mismatches and their score. The function get these parameters: ...

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 ...

Abstract: Graph matching, as an important query technology, has been widely applied in various fields. With the increasing of graph data, users choose to encrypt a large number of graphs and store ...

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 structured ...

We propose a new approach to solve graph isomorphism using parameterized matching. To find isomorphism between two graphs, one graph is linearized, i.e., represented as a graph walk that covers all ...