We present a new algorithm to compute the Graph Edit Distance in a sub-optimal way. We
demonstrate that the distance value is exactly the same than the one obtained by the …

Data granulation emerged as an important paradigm in modeling and computing with
uncertainty, exploiting information granules as the main mathematical constructs involved in …

Bipartite (BP) has been seen to be a fast and accurate suboptimal algorithm to solve the
Error-Tolerant Graph Matching problem. Recently, Fast Bipartite (FBP) has been presented …

The Graph edit distance is the most used distance between attributed graphs and it is
defined as the minimum amount of required distortion to transform one graph into the other …

Bipartite graph matching has been demonstrated to be one of the most efficient algorithms to
solve error-tolerant graph matching. This algorithm is based on defining a cost matrix …

In the presentation of the graph edit distance in 1983 and other newer bibliography, authors
state that it is necessary to apply the distance restrictions (non-negativity, identity of …

Graph edit distance has been used since 1983 to compare objects in machine learning
when these objects are represented by attributed graphs instead of vectors. In these cases …

Given two graphs, the aim of graph matching is to find the׳׳ best׳׳ matching between the
nodes of one graph and the nodes of the other graph. Due to distortions of the data and the …

In pattern recognition, it is usual to compare structured objects through attributed graphs in
which nodes represent local parts and edges relations between them. Thus, each …

This paper introduces a new general-purpose classification system able to face
automatically a wide range of classification problems for labeled graphs. The proposed …