This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in
terms of the expressiveness of the employed similarity measure and the efficiency of its …

The graph edit distance is a widely used distance measure for labelled graph. However,
A★− GED, the standard approach for its exact computation, suffers from huge runtime and …

Currently, there is a" boom" in introductory programming courses to help students develop
their computational thinking skills. Providing timely, personalized feedback that makes …

The graph edit distance (GED) is a well-established distance measure widely used in many
applications, such as bioinformatics, data mining, pattern recognition, and graph …

Graphs are widely used to model complex data in many applications, such as bioinformatics,
chemistry, social networks, pattern recognition. A fundamental and critical query primitive is …

Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory.
Given a graph pair (G 1, G 2), GED is defined as the minimum number of primitive …

Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a
query graph is within a threshold. As GED computation is NP-hard, the existing works adopt …

The boom of cloud computing has stimulated the prevalence of outsourced query services,
and privacy concerns further motivate extensive studies on privacy-preserving queries in the …

The problem of k-nearest neighbor (k-NN) search is fundamental in graph databases, which
has numerous real-world applications, such as bioinformatics, computer vision, and software …