Graph Model Research Articles

On damping a control system with global aftereffect on quantum graphs: Stochastic interpretation

Quantum graphs model processes in complex systems represented as spatial networks in various fields of natural science and technology. An example is the oscillations of elastic string networks, the nodes of which, besides the continuity conditions, also obey the Kirchhoff conditions, expressing the balance of tensions. In this paper, we propose a new look at quantum graphs as temporal networks, which means that the variable parametrizing the edges of a graph is interpreted as time, while each internal vertex is a branching point giving several different scenarios for the further trajectory of a process. Then Kirchhoff‐type conditions may also arise. Namely, they will be satisfied by such a trajectory of the process that is optimal with account of all the scenarios simultaneously. By employing the recent concept of global delay, we extend the problem of damping a first‐order control system with aftereffect, considered earlier only on an interval, to an arbitrary tree graph. The first means that the delay, imposed starting from the initial moment of time, associated with the root of the tree, propagates through all internal vertices. Bringing the system into the equilibrium and minimizing the energy functional with account of the anticipated probability of each scenario, we come to a variational problem. Then, we establish its equivalence to a self‐adjoint boundary value problem on the tree for some second‐order equations involving both the global delay and the global advance. The unique solvability of both problems is proved. We also illustrate that the interval case when the coefficients of the equation are discrete stochastic processes in discrete time can be viewed as the extension to a tree.

On the edge: a multilevel perspective on innovation complexities and dynamic attractors in the supply network

PurposeIn innovation management, the complexity inherited in the supply network may be necessary for success. This study aims to holistically examine innovation complexities and system attractors within a hierarchically nested supply network and explore how they dynamically interact and influence adaptive innovation processes.Design/methodology/approachTaking a complexity theory perspective, we employed a methodological bricolage approach using a single case study with multiple embedded units of analysis – namely, a supply network encompassing 36 firms. We drew upon primary data obtained from 42 interviewees and rich secondary data, and we employed a temporal exponential random graph model to examine the micro-foundations of the evolution of the sampled supply network over a decade.FindingsThis study presents a comprehensive overview of the innovation complexities—relational, temporal, dynamic, operational and structural – and how they manifest within a supply network. It also identifies three systemic attractors – point, periodic and strange – and elucidates their relationships with the complexities and their impact on innovative supply network dynamics. The resulting conceptual framework and working propositions provide a detailed perspective on the complex interplay between balanced order and chaos and the potentially unbalanced innovation states within a supply network.Originality/valueThis research offers an in-depth perspective on the innovation complexities and dynamic attractors within a supply network from a holistic, multilevel perspective. It advances complexity theory and deepens the understanding of supply networks as complex adaptive systems.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

Matrix representation of the graph model for conflict resolution based on intuitionistic preferences with applications to trans-regional water resource conflicts in the Lancang–Mekong River Basin

With the continual growth in global demand for water resources, the governance of trans-regional water resources has become an important field of cooperation between countries, and conflicts caused by competition for water resources have become a focal issue in international relations. The root causes of trans-regional water resource conflicts include unclear ownership of water resources and the limited rationality of the parties involved. The water rights trading model has proven to play a stabilizing role in resolving trans-regional water resource conflicts versus the traditional negotiation model. To demonstrate the rationality and theoretical strength of the water rights trading model in allocating trans-regional water resources in uncertain environments, this paper proposes a novel matrix representation of intuitionistic stability definitions through a graph model for conflict resolution (GMCR). First, decision-makers’ (DMs’) intuitionistic preferences, unilateral movements, and intuitionistic unilateral improvements in a GMCR with two DMs are represented in a matrix. The joint unilateral movements and joint intuitionistic unilateral improvements for a coalition in a GMCR with multiple DMs are also represented. Next, according to the logical forms of intuitionistic stability definitions, four stability definitions in a GMCR with intuitionistic preferences were redefined to enable matrix representations of graph models with either two DMs or multiple DMs. Finally, we analyzed the state transitions and strategies of all stakeholders in trans-regional water resource conflicts in the Lancang-Mekong River Basin within the intuitionistic graph model. The contributions of this paper are twofold. First, in terms of theoretical research, this article enriches and develops the GMCR and proposes a set of definitions of matrix intuitionistic stability. Second, in terms of practical application, the intuitionistic stability analysis results can provide a reference for water rights trading to solve trans-regional water resource conflicts in uncertain environments and transform trans-regional water resource governance systems.

The scaling limit of random cubic planar graphs

AbstractWe study the random cubic planar graph with an even number of vertices. We show that the Brownian map arises as Gromov–Hausdorff–Prokhorov scaling limit of as tends to infinity, after rescaling distances by for a specific constant . This is the first time a model of random graphs that are not embedded into the plane is shown to converge to the Brownian map. Our approach features a new method that allows us to relate distances on random graphs to first‐passage percolation distances on their 3‐connected core.

Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

Graph Model for Conflict Resolution for Mixed-Stability Combinatorial Foresight Based on the Combination of Regret Theory and VIKOR Method

Graph Model for Conflict Resolution for Mixed-Stability Combinatorial Foresight Based on the Combination of Regret Theory and VIKOR Method

Advancements in Topological Approaches via Core Minimal Neighborhoods and Their Applications

Graph theory provides many topological systems for modelling blood circulation. The main object is determining the best topology for a successful correct diagnosis. This work illustrates the justification for using topology, rough sets, and graph analysis through neighborhoods. Generalization for an approximation space and a model of the topological graph is presented. Investigating core minimal neighborhoods is essential for categorizing subsets and computing, these techniques perform better than current techniques while maintaining Pawlak’s characteristics. This work presents a method for generalizing rough sets utilizing core minimal neighborhoods using binary relations. Moreover, we will construct four types of dual approximations concerning core minimal neighborhoods as lower and upper approximations. A comparison between different types of dual approximations is discussed. Core minimal neighborhoods induce certain types of topological structures. Finally, we compare different topologies that assist us in determining the main parts of a human heart’s graph.

A semantic knowledge database-based localization method for UAV inspection in perceptual-degraded underground mine

Abstract In recent years, unmanned aerial vehicles (UAVs) have been applied in underground mine inspection and other similar works depending on their versatility and mobility. However, accurate localization of UAVs in perceptually degraded mines is full of challenges due to the harsh light conditions and similar roadway structures. Due to the unique characteristics of the underground mines, this paper proposes a semantic knowledge database-based localization method for UAVs. By minimizing the spatial point-to-edge distance and point-to-plane distance, the relative pose constraint factor between keyframes is designed for UAV continuous pose estimation. To reduce the accumulated localization errors during the long-distance flight in a perceptual-degraded mine, a semantic knowledge database is established by segmenting the intersection point cloud from the prior map of the mine. The topological feature of the current keyframe is detected in real time during the UAV flight. The intersection position constraint factor is constructed by comparing the similarity between the topological feature of the current keyframe and the intersections in the semantic knowledge database. Combining the relative pose constraint factor of LiDAR keyframes and the intersection position constraint factor, the optimization model of the UAV pose factor graph is established to estimate UAV flight pose and eliminate the cumulative error. Two UAV localization experiments conducted on the simulated large-scale Edgar Mine and a mine-like indoor corridor indicate that the proposed UAV localization method can realize accurate localization during long-distance flight in degraded mines.

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

Latest advancements and prospects in the next-generation of Internet of Things technologies

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Application of quantum graph theory to metamaterial design: Negative refraction of acoustic waveguide modes

We leverage quantum graph theory to quickly and accurately characterize acoustic metamaterials comprising networks of interconnected pipes. Anisotropic bond lengths are incorporated in the model that correspond to space-coiled acoustic structures to exhibit dispersion spectra reminiscent of hyperbolic metamaterials. We construct two metasurfaces with embedded graph structure and, motivated by the graph theory, infer and fine-tune their dispersive properties to engineer nonresonant negative refraction of acoustic surface waves at their interface. Agreement between the graph model, full-wave simulations, and experiments bolsters quantum graph theory as a new paradigm for metamaterial design. Published by the American Physical Society 2024

Development of a Knowledge Base for Construction Risk Assessments Using BERT and Graph Models

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety.

Encoder embedding for general graph and node classification

Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves asymptotic normality on a per-class basis, enabling optimal classification through discriminant analysis. These theoretical findings are validated through a series of experiments involving weighted graphs, as well as text and image data transformed into general graph representations using appropriate distance metrics.

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

Graph total variation and low-rank regularization for heterogeneous change detection

Heterogeneous change detection (HCD) is challenging because different imaging mechanisms for various sensors make images difficult to compare directly. To address this problem, a graph-based regression algorithm is proposed for HCD, by leveraging the Graph Total Variation regularization and Low-Rank matrices decomposition (GTVLR). Utilizing graph signal processing (GSP) theory, a directed graph (digraph) model is employed to effectively represent the orientation and correlation information of images, thereby enabling direct comparison of heterogeneous data within the same domain after graph filtering. The GTVLR framework facilitates the decomposition of post-event images into regression and changed images. This decomposition ensures that the regression image mirrors the structure similarity of the pre-event image, while the changed image highlights areas of alteration, aiding in change detection. The model characterizes the piecewise smoothness and Low-Rank properties of data through GTV regularization and Low-Rank penalty, respectively. Moreover, by integrating the higher-order neighboring information within the digraph to refine the model. Experiments conducted on three real-world datasets and comparison with several state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Asymptotic Analysis of k-Hop Connectivity in the 1D Unit Disk Random Graph Model

Asymptotic Analysis of k-Hop Connectivity in the 1D Unit Disk Random Graph Model

Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder–Decoder Network

The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods.

Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.