Upper Bounding GED via Transformations to LSAPE Based on Rings and Machine Learning

06/29/2019
by   David B. Blumenthal, et al.
0

The graph edit distance (GED) is a flexible distance measure which is widely used for inexact graph matching. Since its exact computation is NP-hard, heuristics are used in practice. A popular approach is to obtain upper bounds for GED via transformations to the linear sum assignment problem with error-correction (LSAPE). Typically, local structures and distances between them are employed for carrying out this transformation, but recently also machine learning techniques have been used. In this paper, we formally define a unifying framework LSAPE-GED for transformations from GED to LSAPE. We introduce rings as a new kind of local structures that are able to capture a lot of information encoded in the input graphs at a low computational cost. Furthermore, we propose two new ring based heuristics RING and RING-ML, which instantiate LSAPE-GED using the traditional and the machine learning based approach for transforming GED to LSAPE, respectively. Extensive experiments show that using rings for upper bounding GED significantly improves the state of the art on datasets where most information resides in the graphs' topologies.

READ FULL TEXT
research
08/01/2019

New Techniques for Graph Edit Distance Computation

Due to their capacity to encode rich structural information, labeled gra...
research
04/18/2019

Convex Graph Invariant Relaxations For Graph Edit Distance

The edit distance between two graphs is a widely used measure of similar...
research
07/05/2019

Improved local search for graph edit distance

Graph Edit Distance (GED) measures the dissimilarity between two graphs ...
research
11/15/2021

EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases

The graph edit distance is an intuitive measure to quantify the dissimil...
research
10/04/2021

Metric Indexing for Graph Similarity Search

Finding the graphs that are most similar to a query graph in a large dat...
research
11/21/2021

Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations

Software clone detection identifies similar code snippets. It has been a...
research
07/27/2023

Bounding the Interleaving Distance for Geometric Graphs with a Loss Function

A geometric graph is an abstract graph along with an embedding of the gr...

Please sign up or login with your details

Forgot password? Click here to reset