• Corpus ID: 7819168

Graph edit distance : a new binary linear programming formulation

@article{Lerouge2015GraphED,
  title={Graph edit distance : a new binary linear programming formulation},
  author={Julien Lerouge and Zeina Abu-Aisheh and Romain Raveaux and Pierre H{\'e}roux and S{\'e}bastien Adam},
  journal={ArXiv},
  year={2015},
  volume={abs/1505.05740}
}
Graph edit distance (GED) is a powerful and flexible graph matching paradigm that can be used to address dierent tasks in structural pattern recognition, machine learning, and data mining. In this paper, some new binary linear programming formulations for computing the exact GED between two graphs are proposed. A major strength of the formulations lies in their genericity since the GED can be computed between directed or undirected fully attributed graphs (i.e. with attributes on both vertices… 

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References

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Approximate graph edit distance computation by means of bipartite graph matching
A binary linear programming formulation of the graph edit distance
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TLDR
A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem, and the new metric is shown to perform quite well in comparison to existing metrics when applications to a database of chemical graphs.
Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic
TLDR
The idea is to use a fast but suboptimal bipartite graph matching algorithm as a heuristic function that estimates the future costs so that it is guaranteed to return the exact graph edit distance of two given graphs.
Bipartite Graph Matching for Computing the Edit Distance of Graphs
TLDR
This paper proposes an approach for the efficient compuation of edit distance based on bipartite graph matching by means of Munkres' algorithm, sometimes referred to as the Hungarian algorithm, which runs in polynomial time, but provides only suboptimal edit distance results.
Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching
TLDR
This paper proposes a novel approach for the efficient computation of graph edit distance based on bipartite graph matching by means of the Volgenant-Jonker assignment algorithm, which provides only suboptimal edit distances, but runs in polynomial time.
Comparing Stars: On Approximating Graph Edit Distance
TLDR
Three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time are introduced and result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs.
A graph matching method and a graph matching distance based on subgraph assignments
Fast Suboptimal Algorithms for the Computation of Graph Edit Distance
TLDR
This paper proposes two simple, but effective modifications of a standard edit distance algorithm that allow us to suboptimally compute edit distance in a faster way and demonstrates the resulting speedup and shows that classification accuracy is mostly not affected.
A survey of graph edit distance
TLDR
The research advance of G ED is surveyed in order to provide a review of the existing literatures and offer some insights into the studies of GED.
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