1,384 Results Found

Presenting real-world paths in property graphs is a complex challenge of identifying and representing the properties of routes and their environments. These property graphs serve as foundational datasets for generating smart sports training routes, w...

Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, a...

In the field of materials science, experimental data or simulation results on material properties are often unevenly distributed. In addition to the vast unexplored material space, properties of lesser interest have not been measured even for well-st...

The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been...

Graph-based neural networks have proven to be useful in molecular property prediction, a critical component of computer-aided drug discovery. In this application, in response to the growing demand for improved computational efficiency and localized e...

We prove a version of the Ekeland Variational Principle (EkVP) in a weighted graph G and its equivalence to Caristi fixed point theorem and to the Takahashi minimization principle. The usual completeness and topological notions are replaced with some...

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties o...

Feature extraction is essential for chemical property estimation of molecules using machine learning. Recently, graph neural networks have attracted attention for feature extraction from molecules. However, existing methods focus only on specific str...

Molecular property prediction, as one of the important tasks in cheminformatics, is attracting more and more attention. The structure of a molecule is closely related to its properties, and a symmetrical molecular structure may differ significantly f...

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information fr...

In this paper, the high-pressure physical property analysis of reservoir fluid PVT (pressure–volume–temperature) was studied to improve the accuracy and efficiency of reservoir fluid property prediction. Due to the limitations of traditio...

Spectral graph theory is like a special tool for understanding graphs. It helps us find patterns and connections in complex networks, using the magic of eigenvalues. Let $\mathsf{G}$ be the graph and $\mathsf{A}(\mathsf{G})$ be its adjacency matrix, then $\mathsf{G}$ is singular if the deter...

Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of...

Let $G=(V,E)$ be a simple connected graph with vertex set V and edge set E, respectively. The term “anti-reciprocal eigenvalue property“ refers to a non-singular graph G for which, $-\frac{1}{\lambda }\in \sigma \left(G\right)$, whenever $\lambda \in \mathrm{\&s}$...

This paper discusses an evaluation method of transmission properties of networks described with regular graphs (Reference Graphs) using unevenness coefficients. The first part of the paper offers generic information about describing network topology...

This paper extends the concept of the total graph $T_{\mathsf{\Gamma }}\left(R\right)$ associated with a commutative ring to the three-fold Cartesian product $R={\mathbb{Z}}_n\times {\mathbb{Z}}_m\times {\mathbb{Z}}_p$, where $n,m,p>1$. We present complete and self-contained proofs for a wide range of graph-the...

During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selec...

Claude Berge (1987) introduced the concept of k-extendable graphs, wherein any independent set of size k is inherently a constituent of a maximum independent set within a graph $H=(V,E)$. Graphs possessing the property of being 1-extendable are termeda...

Despite the growing adoption of Property Graph Databases, like Neo4j, interacting with them remains difficult for non-technical users due to the reliance on formal query languages. Natural Language Interfaces (NLIs) address this by translating natura...

Graph entropy plays an essential role in interpreting the structural information and complexity measure of a network. Let G be a graph of order n. Suppose $d_G\left(v_i\right)$ is degree of the vertex $v_i$ for each $i=1,2,\dots ,n$. Now, the k-th degree-based graph e...

The prediction of mechanical properties of cold-rolled steel is very important for the quality control, process optimization, and cost control of cold-rolled steel, but it is still a challenging task to predict accurately. For the existing graph stru...

Knowledge graphs are a popular research field in artificial intelligence, and store large amounts of real-world data. Since data are enriched over time, the knowledge graph is often incomplete. Therefore, knowledge graph completion is particularly im...

Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based o...

We study the emergence of cooperation in structured populations with any arrangement of cooperators and defectors on the evolutionary graph. In a computational approach using structure coefficients defined for configurations describing such arrangeme...

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discon...

An irregularity index $IR\left(\mathsf{\Gamma }\right)$ of a graph $\mathsf{\Gamma }$ is a nonnegative numeric quantity (i.e., $IR\left(\mathsf{\Gamma }\right)\ge 0$) such that $IR\left(\mathsf{\Gamma }\right)=0$ iff $\mathsf{\Gamma }$ is a regular graph. In this paper, we show that $IRC$ closely correlates with the normal boiling point $T_{}$...

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the m...

Information exchange between building information modelling (BIM) and geographic information system (GIS) is problematic, especially in terms of semantic information. Graph-based technologies, such as the resource description framework (RDF) and the...

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with...

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for represent...

Today, as PHP application technology is becoming increasingly mature, the functions of modern multi-layer web applications are becoming more and more complete, and the complexity is also gradually increasing. While providing developers with various b...

This paper introduces the Accumulated Concept Graph (ACG), a visualization tool based on the quantile method designed to analyze three-way data, including distributional data. Such data often have complex structures that make it difficult to identify...

Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed graph neural network (GNN) a...

In this work, the concept of equitable partition is applied to molecular graphs to define quantitative descriptors of the symmetry and complexity of molecules, with the ultimate goal of shedding new light on the structure–property relationships...

A graph is chordal if every induced cycle has exactly three edges. A vertex separator set in a graph is a set of vertices that disconnects two vertices. A graph is $\delta$ -hyperbolic if every geodesic triangle is $\delta$ -thin. In this paper, we study...

This article proposes a novel approach that leverages graph theory, machine learning, and graph embedding to evaluate research groups comprehensively. Assessing the performance and impact of research groups is crucial for funding agencies and researc...

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks...

Layered algebras are introduced and used to express layered graphs. Layered graphs are considered to be a highly effective abstract tool to manage the difficulty in conceptualizing and reasoning regarding complex systems related to coding in email ex...

We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate $(\delta +\alpha )$. The method combines (i) a wedge-sampling tester that estima...

Graphs play a pivotal role in structuring real-world scenarios such as network security and expert systems. Numerous extensions of graph theoretical conceptions have been established for modeling uncertainty in graphical network situations. The Pytha...

Arundo donax (L.) is a perennial monocot (Poaceae) native to Asia, which has spread throughout the Mediterranean region. Its hollow stem has been used for millennia to produce reeds, thin strips whose vibration is modulated by musical instruments suc...

In the paper, the crossing number of the join product $G^{*}+D_n$ for the disconnected graph $G^{*}$ consisting of two components isomorphic to $K_2$ and $K_3$ is given, where $D_n$ consists of n isolated vertices. Presented proofs are completed with the help of the gra...

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkage...

The method of constructing the generalized dihedral group as a semidirect product of an abelian group and the group ${\mathbb{Z}}_2$ of integers modulo 2 is extended to the case of gyrogroups. This leads to the study of a new class of gyrogroups, which includes ge...

Model checking has become a widely used and precise technique for verifying software systems. However, a major challenge in model checking is state space explosion, which occurs due to the exponential memory usage required by the model checker. To ad...

The aim of this study was to investigate the characteristic polynomials resulting from the molecular graphs used as molecular descriptors in the characterization of the properties of chemical compounds. A formal calculus method is proposed in order t...

Accurate predictions of molecular properties are crucial for advancements in drug discovery and materials science. However, this task is complex and requires effective representations of molecular structures. Recently, Graph Neural Networks (GNNs) ha...

Gromov hyperbolicity is an interesting geometric property, and so it is natural to study it in the context of geometric graphs. In particular, we are interested in interval and indifference graphs, which are important classes of intersection and Eucl...

We define the signed Cayley graph on Cayley graph $X_n$ denoted by $S_n$, and study several properties such as balancing, clusterability and sign-compatibility of the signed Cayley graph $S_n$. Apart from it we also study the characterization of the canonical...

In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is pro...