Greed: A neural framework for learning graph distance functions
Similarity search in graph databases is one of the most fundamental operations in graph
analytics. Among various distance functions, graph and subgraph edit distances (GED and …
analytics. Among various distance functions, graph and subgraph edit distances (GED and …
Comparing heuristics for graph edit distance computation
Because of its flexibility, intuitiveness, and expressivity, the graph edit distance (GED) is one
of the most widely used distance measures for labeled graphs. Since exactly computing …
of the most widely used distance measures for labeled graphs. Since exactly computing …
[PDF][PDF] Network medicine-based epistasis detection in complex diseases: ready for quantum computing
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture,
it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic …
it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic …
Towards accurate subgraph similarity computation via neural graph pruning
Subgraph similarity search, one of the core problems in graph search, concerns whether a
target graph approximately contains a query graph. The problem is recently touched by …
target graph approximately contains a query graph. The problem is recently touched by …
[HTML][HTML] Scalable generalized median graph estimation and its manifold use in bioinformatics, clustering, classification, and indexing
In this paper, we present GMG-BCU—a local search algorithm based on block coordinate
update for estimating a generalized median graph for a given collection of labeled or …
update for estimating a generalized median graph for a given collection of labeled or …
Efficient parallel branch-and-bound approaches for exact graph edit distance problem
Abstract Graph Edit Distance (GED) is a well-known measure used in the graph matching to
measure the similarity/dissimilarity between two graphs by computing the minimum cost of …
measure the similarity/dissimilarity between two graphs by computing the minimum cost of …
A graph pre-image method based on graph edit distances
The pre-image problem for graphs is increasingly attracting attention owing to many
promising applications. However, it is a challenging problem due to the complexity of graph …
promising applications. However, it is a challenging problem due to the complexity of graph …
Graph Edit Distance with General Costs Using Neural Set Divergence
Graph Edit Distance (GED) measures the (dis-) similarity between two given graphs, in terms
of the minimum-cost edit sequence that transforms one graph to the other. However, the …
of the minimum-cost edit sequence that transforms one graph to the other. However, the …
A Study on the Stability of Graph Edit Distance Heuristics
Graph edit distance (GED) is a powerful tool to model the dissimilarity between graphs.
However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods …
However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods …
EUGENE: Explainable Unsupervised Approximation of Graph Edit Distance
A Bommakanti, HR Vonteri, S Ranu… - arxiv preprint arxiv …, 2024 - arxiv.org
The need to identify graphs having small structural distance from a query arises in biology,
chemistry, recommender systems, and social network analysis. Among several methods to …
chemistry, recommender systems, and social network analysis. Among several methods to …