Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
Vectorial Graph Distance
View through CrossRef
Abstract
We introduce a Vectorial Graph Distance (VGD), a distance measure between graphs, a novel metric that extends our previously defined Vectorial Tree Distance (VTD) to general undirected, unweighted graphs. The method converts each graph into a unique tree by combining a bounded quotient graph, constructed using node-betweenness centrality, with a modified breadth-first search (BFS) that ensures uniqueness. Once converted, the resulting trees are compared using the VTD, yielding a structured, vector-valued distance that reflects hierarchical differences between graphs. We further demonstrate the utility of the VGD by applying it to the ENZYMES dataset (BRENDA), showing that it successfully distinguishes molecular graphs from different enzyme classes. The approach captures meaningful structural distinctions and provides an interpretable, multi-level measure of graph similarity.
Title: Vectorial Graph Distance
Description:
Abstract
We introduce a Vectorial Graph Distance (VGD), a distance measure between graphs, a novel metric that extends our previously defined Vectorial Tree Distance (VTD) to general undirected, unweighted graphs.
The method converts each graph into a unique tree by combining a bounded quotient graph, constructed using node-betweenness centrality, with a modified breadth-first search (BFS) that ensures uniqueness.
Once converted, the resulting trees are compared using the VTD, yielding a structured, vector-valued distance that reflects hierarchical differences between graphs.
We further demonstrate the utility of the VGD by applying it to the ENZYMES dataset (BRENDA), showing that it successfully distinguishes molecular graphs from different enzyme classes.
The approach captures meaningful structural distinctions and provides an interpretable, multi-level measure of graph similarity.
Related Results
Graph convolutional neural networks for 3D data analysis
Graph convolutional neural networks for 3D data analysis
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning p...
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Penelitian ini bertujuan menentukan bilangan terhubung titik pelangi (rainbow vertex connection number) pada graf garis dan graf tengah yang diperoleh dari hasil operasi comb antar...
Bootstrapping a Biodiversity Knowledge Graph
Bootstrapping a Biodiversity Knowledge Graph
The "biodiversity knowledge graph" is a nice metaphor for connecting biodiversity data sources, but can we actually build it? Do we have sufficient linked data available? Given tha...
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract
Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for captu...
Domination of Polynomial with Application
Domination of Polynomial with Application
In this paper, .We .initiate the study of domination. polynomial , consider G=(V,E) be a simple, finite, and directed graph without. isolated. vertex .We present a study of the Ira...
E-Cordial Labeling of Some Families of Graphs
E-Cordial Labeling of Some Families of Graphs
An E-cordial labeling σ: E →{0,1} induces σ∗: V →{0,1} on graph G=(V,E), where (σ(v)=(∑_(u∈V)▒〖σ(uv)〗) mod 2 is taken over all edges uv∈E, and the labelling satisfies the condition...
The quadrangle graph operator
The quadrangle graph operator
The cycle graph of a graph G is the graph [Formula: see text] whose vertices are the induced cycles of G and where two vertices are adjacent if and only if they are distinct induce...
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent gr...