Graph Applications Research Articles

Application of Complex Neutrosophic Graphs in Hospital Infrastructure Design

Complex neutrosophic graphs are created by combining complex neutrosophic sets with graph theory ideas. This provides a flexible framework for tackling complex problem-solving circumstances. Various processes, such as union, join, and composition, are thoroughly investigated to improve the administration of complicated neutrosophic graphs. This research also looks into the area of complicated neutrosophic graph homeomorphisms, investigating the transformations and mappings that occur inside these structures. This investigation advances our knowledge of the intrinsic features and linkages found in complicated neutrosophic graphs. Finally, our methodology’s practical usefulness is demonstrated by its use in the design of hospital infrastructure. We illustrate the effectiveness of complicated neutrosophic graphs in addressing difficult design concerns and optimizing infrastructure solutions for practical deployment in this real-world scenario.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PurposeGraph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring’s effectiveness in computational network biology, more precisely in analyzing protein–protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.MethodsTo investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.ResultsWe first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.ConclusionOur generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Effectively answering why questions on structural graph clustering

The pSCAN algorithm is widely acknowledged for its efficiency in structural graph clustering across diverse graph applications, frequently utilized to detect significant clusters within graphs. The results of pSCAN are constrained by two parameters: (1) the structural similarity constraint ϵ; (2) the density constraint μ. Nevertheless, determining appropriate values for these parameters proves challenging as users often lack the requisite professional knowledge. Consequently, users may inquire about the inclusion of unexpected vertices in specified clusters, posing why questions. In this paper, we aim to address these inquiries by investigating the challenge of answering why questions on structural graph clustering. The objective is to refine the initialized clustering parameters to exclude unexpected vertices from specified clusters. Initially, we introduce the crucial concept of label-vertices and delve into a unified explanation framework designed for addressing why questions on structural graph clustering. Subsequently, we propose two effective refining algorithms, namely FReEPS and FReMU, specifically tailored to modify the similarity constraint ϵ and the density constraint μ independently. Moreover, in our approach to enhancing efficiency, we introduce two baseline explanation algorithms, ReParFirstMUand ReParFirstEPS, simultaneously refining the parameters ϵ and μ to exclude unexpected vertices from specified clusters. Additionally, we explore two improved algorithms, FReParFirstMUand FReParFirstEPS, incorporating a process of pruning unnecessary parameter combinations and computations. This enhancement aims to improve the efficiency of simultaneously refining the two parameters for addressing these questions. Finally, we conducted comprehensive experiments on real social network datasets. The results illustrate that the explanation efficiency of FReEPS and FReMU surpasses that of the state-of-the-art explanation algorithms by 1-2 orders of magnitude. Moreover, FReMu exhibits faster performance than FReEPS. Furthermore, simultaneous refinement of the parameters ϵ and μ yields better-refined parameters. Additionally, FReParFirstMU and FReParFirstEPS outperform ReParFirstMU and ReParFirstEPS, respectively.

ConTIG: Continuous representation learning on temporal interaction graphs

Representation learning on temporal interaction graphs (TIG) aims to model complex networks with the dynamic evolution of interactions on a wide range of web and social graph applications. However, most existing works on TIG either (a) rely on discretely updated node embeddings merely when an interaction occurs that fail to capture the continuous evolution of embedding trajectories of nodes, or (b) overlook the rich temporal patterns hidden in the ever-changing graph data that presumably lead to sub-optimal models. In this paper, we propose a two-module framework named ConTIG, a novel representation learning method on TIG that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploits three-fold factors in dynamic networks including latest interaction, neighbor features, and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories from time-adjacent interaction patterns using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiment results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation, and dynamic node classification tasks of four datasets compared with a range of state-of-the-art baselines, especially for long-interval interaction prediction.

Picture fuzzy cubic graphs and their applications

In the realm of handling imprecise information, picture fuzzy cubic sets have emerged as a more versatile tool compared to cubic sets, cubic intuitionistic fuzzy sets, and similar models. These sets offer better adaptability, precision and compatibility with the system than existing fuzzy models. This paper extends the concept of picture fuzzy cubic sets to the domain of graph theory, introducing the novel concept of picture fuzzy cubic graphs that surpasses previous results in terms of generality. The paper explores various essential operations, including composition, the Cartesian product, P-join, R-join, P-union, R-union of picture fuzzy cubic graphs. It also investigates the order and degree of picture fuzzy cubic graphs. Furthermore, this work presents two practical applications of picture fuzzy cubic graphs. The first application involves computing the impact of other companies on a specific company and the second application focuses on evaluating the overall impact within a group of companies.

Structures and applications of graphs arising from congruences over moduli

<p>For any positive integer $ \mathrm{n} $, let $ \mathrm{M_{p}} $ contain the prime numbers less than $ \mathrm{n} $. Assuming $ \mathrm{M_{p}} $ as the set of moduli, we draw a graph with the vertex set $ \{1, 2, 3, \cdots, \mathrm{n}\} $, and an edge will be built between the vertices $ \mathrm{p} $ and $ \mathrm{q} $ if and only if $ \mathrm{p}\equiv (\mathrm{q}\; mod\; \mathrm{m}) $ for some $ \mathrm{m}\in $ $ \mathrm{M_{p}}. $ We call this graph a prime congruence simple graph and label this graph as $ \mathrm{G}(\mathrm{n}, \mathrm{M}_{p}) $. The objective of this work is to characterize prime congruence simple graphs, and afterwards, by utilizing these graphs, solutions to the system of linear congruences are suggested and demonstrated by applying modular arithmetic. It is shown that this graph is always a connected graph. The generalized formulae for vertex degrees, size, chromatic number, domination number, clique number, and eccentricity of the prime congruence simple graphs are proposed and proved. Also, independence numbers as well as a covering number for the proposed graph through vertices and edges are evaluated. Lastly, as an application of prime congruence simple graphs, the solution of a system of linear congruences is discussed in terms of the degrees of the vertices.</p>

Research on the Application of Knowledge Graph in Demand-Side Flexible Resource Profiling and Aggregation Techniques

Abstract This paper commences by assessing the current landscape of power system development, focusing on the theory, principles, and structures of demand-side flexible resources and their aggregation technology. Utilizing network crawler technology within a knowledge graph framework, the research data pertinent to demand-side flexible resources and aggregation technology are extracted. These data undergo a meticulous cleaning process before being stored, culminating in the development of a knowledge graph tailored to the imaging and technology of demand-side flexible resources. The findings reveal a response rate of 7.25% ± 1.15%, with an uncertainty interval of 2.33%. Variations in air-conditioning load states appear to exert minimal impact on the response time lag. Following the issuance of a response signal, all systems can rapidly initiate appropriate response actions, demonstrating an uncertainty interval of approximately 52s±11s and 22s. The duration of the responses averages around 75s±11s, with an uncertainty interval of about 30s. This study fulfills the power system criteria for standards of demand-side flexible resources and augments the competitiveness of China’s power market.

Integrated Application of LLM Model and Knowledge Graph in Medical Text Mining and Knowledge Extraction

Integrated Application of LLM Model and Knowledge Graph in Medical Text Mining and Knowledge Extraction

Construction and Application of User Check-in Spatiotemporal Knowledge Graph Based on Neo4j

With the rapid acceleration of urbanization and the widespread adoption of social networks, A vast amount of spatiotemporal check-in data from social media users has been generated. Traditional relational databases have limitations in handling massive spatiotemporal data, whereas graph databases, with their efficiency in data querying and processing, offer a novel solution for the analysis of spatiotemporal data. The paper introduces a method for constructing a user check-in spatiotemporal knowledge graph based on the Neo4j graph database, utilizing a designed graph model to create a visual knowledge graph. By incorporating temporal and spatial characteristics into entities, we realize efficient management and complex query of spatiotemporal data of social media user check-ins. By adding sentiment attributes to relationships through sentiment analysis of check-in text, the application of check-in text is realized. Ultimately, employing Cypher to achieve knowledge querying, we analyze the hot spots of urban activities and categories.

A comprehensive review of the data and knowledge graphs approaches in bioinformatics

The scientific community is currently showing strong interest in constructing knowledge graphs from heterogeneous domains (genomic, pharmaceutical, clinical etc.). The main goal here is to support researchers in gaining an immediate overview of the biomedical and clinical data that can be utilized to construct and extend KGs. A in-depth overview of the available biomedical data and the latest applications of knowledge graphs, from the biological to the clinical context, is provided showing the most recent methods of representing biomedical knowledge with embeddings (KGEs). Furthermore, this review, differentiates biomedical databases based on their construction process (whether manually curated by experts or not), aiming to offer a detailed overview and guide researchers in selecting the appropriate database for their research considering to the specific project needs, available resources, and data complexity. In conclusion, the review highlights current challenges: integration of different knowledge graphs and the interpretability of predictions of new relations.

Research and Application of Knowledge Graph Design for Interactive Teaching Platform Based on Artificial Intelligence

Abstract With the rapid development of education digitization, artificial intelligence (AI) and knowledge graph, their application in the field of education has received extensive attention and research by many scholars. In this paper, the design of knowledge graphs for AI teaching platforms is studied. Based on the construction process of a general knowledge graph, teaching resources’ text is processed through the steps of knowledge entity recognition and relationship extraction, which is applied to the intelligent interactive teaching platform. On this basis, the article designs a method for teaching resource retrieval based on semantic analysis to provide a path for further teaching applications of knowledge graphs. After completing its design, application and testing are carried out to test its feasibility and effectiveness. The score of the experimental class that used the knowledge graph increased by 17.68, which is about 22.47% year-on-year compared to the pre-class period; the score of the control class also rose, with a rise of 9.41, which is about 11.53% year-on-year. The results indicate that a knowledge graph-equipped teaching platform can better meet the needs of interactive teaching and enhance students’ learning abilities.

Role Analysis of the Construction and Application of Folk Dance Knowledge Graph in Cultural Transmission

Abstract This paper first introduces ontology construction, knowledge extraction, and graph database in the process of knowledge graph construction based on folk dance. Secondly, the semantic similarity algorithm is used as the basis to construct the conceptual similarity (MD4) model, and the five types of similarity algorithms in the MD4 model are introduced. After that, based on this model, we will conduct a comparative study on the number of folk dance publications, keywords, and hotspots in China and abroad. Finally, suggestions are made for creating a knowledge graph that is based on folk dance. The annual volume of folk dance research published in China is generally increasing. In contrast, the amount of foreign publications on folk dance is generally low, and its maximum number of publications is only 67. In China, 1021 keywords finally got 156 nodes, and the total link strength of the total number of occurrences was 3942. 1352 keywords finally got 137 nodes, and the total link strength of the total number of occurrences was 1983. Among all the keywords in China, the top three co-occurrence-ranked keywords and hot words were folk dance, value, and development status, with a number of 817, 633, and 607, respectively. The foreign countries include folk dance (453), value (233), and performance (146). The cultural inheritance of folk dance can be greatly impacted by the knowledge map’s ability to summarize the development of folk dance.

Identifying Internet Streaming Services using Max Product of Complement in Neutrosophic Graphs

The complement of the highest result of multiplication of two neutrosophic graphs is determined in this study. In the complement of the maximum product of a neutrosophic graph, the degree of a vertex is investigated. The complement of the maximum product of two normal neutrosophic graphs has several results that are presented and proven. Finally, we have offered a neutrosophic graph application for locating an online streaming service using normalized Hamming distance.

Modeling and application of a customized knowledge graph for railway alignment optimization

Railway alignment optimization is a complex process in which human knowledge and experience are extensively used. However, the related knowledge is usually unstructured, which is difficult for computers to recognize. Moreover, related knowledge is applied in fragmented ways in existing alignment optimization methods, which are thus difficult to update with actual advancements of human experience and knowledge. To solve the above problems, the first-known knowledge graph modeling method for railway alignment optimization is proposed in this paper. First, a hierarchical and categorized semantic network modeling approach for railway alignment design knowledge is devised. Based on this, a railway alignment design knowledge graph (RAD-KG) is constructed. Then, a rapid knowledge retrieval method is proposed for improving the querying efficiency from the RAD-KG during alignment optimization. Finally, the RAD-KG integrating multiple alignment design principles is successfully applied to a real-world case. It is verified that the alignment generated by the proposed method reduces costs by 9.2% compared with conventional manual work by experienced engineers. Moreover, the RAD-KG-assisted method can rapidly update alignment design guidelines during optimization and, hence, produce several alignment alternatives satisfying various complicated requirements, which confirms the flexibility of the proposed method.

Flip : Data-centric Edge CGRA Accelerator

Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE) and route the data dependencies among the operations through the Network-on-Chip. However, CGRAs are designed for fine-grained static instruction-level parallelism and struggle to accelerate applications with dynamic and irregular data-level parallelism, such as graph processing. To address this limitation, we present Flip , a novel accelerator that enhances traditional CGRA architectures to boost the performance of graph applications. Flip retains the classic CGRA execution model while introducing a special data-centric mode for efficient graph processing. Specifically, it leverages the inherent data parallelism of graph algorithms by mapping graph vertices onto PEs rather than the operations and supporting dynamic routing of temporary data according to the runtime evolution of the graph frontier. Experimental results demonstrate that Flip achieves up to 36× speedup with merely 19% more area compared to classic CGRAs. Compared to state-of-the-art large-scale graph processors, Flip has similar energy efficiency and 2.2× better area efficiency at a much-reduced power/area budget.

Application of knowledge graph in software engineering field: A systematic literature review

Application of knowledge graph in software engineering field: A systematic literature review

Review and Application of Knowledge Graph in Crisis Management

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.

Overview of the application of knowledge graphs in the medical field

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.

Interaction graph learning of line cascading failure in power networks and its statistical properties

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences.

Retracted: Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020

Retracted: Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020