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Journal articles on the topic 'Chain graph models'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Drton, Mathias. "Discrete chain graph models." Bernoulli 15, no. 3 (2009): 736–53. http://dx.doi.org/10.3150/08-bej172.

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Skornyakov, Vladimir, Maria Skornyakova, Antonina Shurygina, and Pavel Skornyakov. "Finite-state discrete-time Markov chain models of gene regulatory networks." F1000Research 3 (September 12, 2014): 220. http://dx.doi.org/10.12688/f1000research.4669.1.

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In this study, Markov chain models of gene regulatory networks (GRN) are developed. These models make it possible to apply the well-known theory and tools of Markov chains to GRN analysis. A new kind of finite interaction graph called a combinatorial net is introduced to represent formally a GRN and its transition graphs constructed from interaction graphs. The system dynamics are defined as a random walk on the transition graph, which is a Markov chain. A novel concurrent updating scheme (evolution rule) is developed to determine transitions in a transition graph. The proposed scheme is based

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Ferrándiz, Juan, Enrique F. Castillo, and Pilar Sanmartín. "Temporal aggregation in chain graph models." Journal of Statistical Planning and Inference 133, no. 1 (2005): 69–93. http://dx.doi.org/10.1016/j.jspi.2004.03.012.

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4

Wang, Yuhao, and Arnab Bhattacharyya. "Identifiability of Linear AMP Chain Graph Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10080–89. http://dx.doi.org/10.1609/aaai.v36i9.21247.

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We study identifiability of linear Andersson-Madigan-Perlman (AMP) chain graph models, which are a common generalization of linear structural equation models and Gaussian graphical models. AMP models are described by DAGs on chain components which themselves are undirected graphs. For a known chain component decomposition, we show that the DAG on the chain components is identifiable if the determinants of the residual covariance matrices of the chain components are equal (or more generally, monotone non-decreasing in topological order). This condition extends the equal variance identifiability

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5

Oluwatumininu Anne Ajayi. "Anticipating supply chain disruptions with graph AI models." World Journal of Advanced Engineering Technology and Sciences 7, no. 1 (2022): 241–44. https://doi.org/10.30574/wjaets.2022.7.1.0095.

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Supply chain networks are increasingly complex and interconnected, making them vulnerable to disruptions caused by natural disasters, geopolitical tensions, cyberattacks, and market volatility. Traditional forecasting and risk management techniques often fall short in dynamically capturing the multi-relational and non-linear dependencies within these networks. This paper explores the role of Graph AI models—particularly Graph Neural Networks (GNNs)—in modeling, predicting, and mitigating supply chain disruptions. We propose a framework for integrating Graph AI into supply chain operations, emp

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Knudsen, Michael, and Carsten Wiuf. "A Markov Chain Approach to Randomly Grown Graphs." Journal of Applied Mathematics 2008 (2008): 1–14. http://dx.doi.org/10.1155/2008/190836.

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A Markov chain approach to the study of randomly grown graphs is proposed and applied to some popular models that have found use in biology and elsewhere. For most randomly grown graphs used in biology, it is not known whether the graph or properties of the graph converge (in some sense) as the number of vertices becomes large. Particularly, we study the behaviour of the degree sequence, that is, the number of vertices with degree0,1,…,in large graphs, and apply our results to the partial duplication model. We further illustrate the results by application to real data.

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Vo, Thi Phuong Thuy. "Chain-referral sampling on stochastic block models." ESAIM: Probability and Statistics 24 (2020): 718–38. http://dx.doi.org/10.1051/ps/2020025.

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The discovery of the “hidden population”, whose size and membership are unknown, is made possible by assuming that its members are connected in a social network by their relationships. We explore these groups by a chain-referral sampling (CRS) method, where participants recommend the people they know. This leads to the study of a Markov chain on a random graph where vertices represent individuals and edges connecting any two nodes describe the relationships between corresponding people. We are interested in the study of CRS process on the stochastic block model (SBM), which extends the well-kn

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8

Lauritzen, Steffen L., and Thomas S. Richardson. "Chain graph models and their causal interpretations." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, no. 3 (2002): 321–48. http://dx.doi.org/10.1111/1467-9868.00340.

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9

KhudaBukhsh, Wasiur R., Arnab Auddy, Yann Disser, and Heinz Koeppl. "Approximate lumpability for Markovian agent-based models using local symmetries." Journal of Applied Probability 56, no. 3 (2019): 647–71. http://dx.doi.org/10.1017/jpr.2019.44.

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AbstractWe study a Markovian agent-based model (MABM) in this paper. Each agent is endowed with a local state that changes over time as the agent interacts with its neighbours. The neighbourhood structure is given by a graph. Recently, Simon, Taylor, and Kiss [40] used the automorphisms of the underlying graph to generate a lumpable partition of the joint state space, ensuring Markovianness of the lumped process for binary dynamics. However, many large random graphs tend to become asymmetric, rendering the automorphism-based lumping approach ineffective as a tool of model reduction. In order t

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Wang, Mingjie, Yifan Huo, Junhong Zheng, and Lili He. "SC-TKGR: Temporal Knowledge Graph-Based GNN for Recommendations in Supply Chains." Electronics 14, no. 2 (2025): 222. https://doi.org/10.3390/electronics14020222.

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Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhance

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Bommasani, Rishi, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, and Percy Liang. "Ecosystem Graphs: Documenting the Foundation Model Supply Chain." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 196–209. http://dx.doi.org/10.1609/aies.v7i1.31629.

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Foundation models (e.g. GPT-4, Gemini, Llama 3) pervasively influence society, warranting greater understanding. While the models garner much attention, accurately characterizing their impact requires considering the broader sociotechnical ecosystem in which they are created and deployed. We propose Ecosystem Graphs as a documentation framework to centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical and social relationships. To supplement the graph structure, each asset is further

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12

Höfler, Michael, Tanja Brückl, Antje Bittner, and Roselind Lieb. "Visualizing Multivariate Dependencies with Association Chain Graphs." Methodology 3, no. 1 (2007): 24–34. http://dx.doi.org/10.1027/1614-2241.3.1.24.

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In a recent paper, a new type of graph to visualize the results from graphical models was proposed. Association chain graphs (ACGs) provide a richer visualization than conventional graphs (directed acyclic and recursive regression graphs) if the data can be described with only a small number of parameters. ACGs display not only which associations reach statistical significance, but also the magnitude of associations (confidence intervals for statistical main effects) as the contrast color to the background color of the graph. In this paper, the ACG visualization is extended especially for the

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13

Charles Chen, Charles Chen, Sai-Sai Shi Charles Chen, and Sheng-Lung Peng Sai-Sai Shi. "A Construction of Knowledge Graph for Semiconductor Industry Chain Based on Lattice-LSTM and PCNN Models." 網際網路技術學刊 25, no. 2 (2024): 313–29. http://dx.doi.org/10.53106/160792642024032502013.

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<p>This paper mainly focuses on building the knowledge graph of semiconductor industry chain. The main research contents include knowledge extraction, knowledge storage, and construction of knowledge graph in semiconductor field. The crawler technology and character recognition technology are used to obtain semiconductor industry chain information from the Internet, magazines, and institutions to establish the original data set. Then, Lattice Long Short-Term Memory (Lattice-LSTM) model is used to implement the entity extraction and recognition. The piecewise convolutional neural network

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14

Dong, Ting, and Mary Jane C. Samonte. "Optimization and Benefit Assessment of Production Supply Chain Networks Using Graph Neural Network Models." Journal of Computing and Information Technology 32, no. 1 (2024): 15–31. http://dx.doi.org/10.20532/cit.2024.1005804.

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With the flourishing development of global economy, effective management of production supply chains is crucial for the competitiveness of enterprises. Optimizing supply chain networks can not only improve the efficiency of resource allocation but also enhance market responsiveness and systemic risk resistance. Traditional supply chain network optimization methods, focusing mostly on linear models and static analysis, fall short in addressing the growing complexity and dynamism. The emergence of Graph Neural Network (GNN) models in recent years has offered new opportunities to tackle non-linea

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15

Anacleto, Osvaldo, and Catriona Queen. "Dynamic Chain Graph Models for Time Series Network Data." Bayesian Analysis 12, no. 2 (2017): 491–509. http://dx.doi.org/10.1214/16-ba1010.

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16

Andersson, Steen A., and Michael D. Perlman. "Characterizing Markov equivalence classes for AMP chain graph models." Annals of Statistics 34, no. 2 (2006): 939–72. http://dx.doi.org/10.1214/009053606000000173.

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17

Ormonova, E. "Analysis of Assessing the Reliability Quality of the Software Product Based on the Graphs Theory and Markov Chains." Bulletin of Science and Practice 6, no. 4 (2020): 12–17. http://dx.doi.org/10.33619/2414-2948/53/01.

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In this paper, we use graph theory and Markov chain methods to determine the quality of a software product based on a mathematical model. Since modern programming, developing a data structure and creating a mathematical model of a software product, we cannot do without graph theory. As an object of research, the PascalABC programming language is used. We have created a mathematical model of a software product using graph theory. The principles of software quality assessment by methods of graph theory, as well as Markov chains, are developed. Models of probabilistic assessment of the quality of

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Ormonova, E. "Analysis of Assessing the Reliability Quality of the Software Product Based on the Graphs Theory and Markov Chains." Bulletin of Science and Practice 6, no. 4 (2020): 12–17. https://doi.org/10.33619/2414-2948/53/01.

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In this paper, we use graph theory and Markov chain methods to determine the quality of a software product based on a mathematical model. Since modern programming, developing a data structure, and creating a mathematical model of a software product, we cannot do without graph theory. As an object of research, the Pascal ABC programming language is used. We have created a mathematical model of a software product using graph theory. The principles of software quality assessment by methods of graph theory, as well as Markov chains, are developed. Models of probabilistic assessment of the qua

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19

Cerqueira, Andressa, Aurélien Garivier, and Florencia Leonardi. "A note on perfect simulation for Exponential Random Graph Models." ESAIM: Probability and Statistics 24 (2020): 138–47. http://dx.doi.org/10.1051/ps/2019024.

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In this paper, we propose a perfect simulation algorithm for the Exponential Random Graph Model, based on the Coupling from the past method of Propp and Wilson (1996). We use a Glauber dynamics to construct the Markov Chain and we prove the monotonicity of the ERGM for a subset of the parametric space. We also obtain an upper bound on the running time of the algorithm that depends on the mixing time of the Markov chain.

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20

Garcia Trelles, Elena, Christoph Schweizer, Akhil Thomas, Philipp von Hartrott, and Marina Janka-Ramm. "Digitalizing Material Knowledge: A Practical Framework for Ontology-Driven Knowledge Graphs in Process Chains." Applied Sciences 14, no. 24 (2024): 11683. https://doi.org/10.3390/app142411683.

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This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the creation of semantic models for various processes along the digital process chain. A generic template for structuring processes is proposed, simplifying subsequent data retrieval. The templates of specific processes are designed collaboratively by domain and ontology experts, aided by a proposed interview template that bridges the knowledge gap. F

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Rezaei, Navid, and Marek Z. Reformat. "Utilizing Language Models to Expand Vision-Based Commonsense Knowledge Graphs." Symmetry 14, no. 8 (2022): 1715. http://dx.doi.org/10.3390/sym14081715.

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The introduction and ever-growing size of the transformer deep-learning architecture have had a tremendous impact not only in the field of natural language processing but also in other fields. The transformer-based language models have contributed to a renewed interest in commonsense knowledge due to the abilities of deep learning models. Recent literature has focused on analyzing commonsense embedded within the pre-trained parameters of these models and embedding missing commonsense using knowledge graphs and fine-tuning. We base our current work on the empirically proven language understandi

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22

Marchetti, Giovanni M., and Monia Lupparelli. "Chain graph models of multivariate regression type for categorical data." Bernoulli 17, no. 3 (2011): 827–44. http://dx.doi.org/10.3150/10-bej300.

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23

Studený, Milan, and Remco R. Bouckaert. "On chain graph models for description of conditional independence structures." Annals of Statistics 26, no. 4 (1998): 1434–95. http://dx.doi.org/10.1214/aos/1024691250.

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24

Mukminov, T. F. "The chain of embedded invariant submodels for conic motions." Multiphase Systems 14, no. 4 (2019): 268–73. http://dx.doi.org/10.21662/mfs2019.4.034.

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The equations of continuum mechanics are invariant in relation to the Galilean group generalized by extention. Its 11-dimensional Lie algebra has many subalgebras, which form the optimal system of dissimilar subalgebras. Subalgebras from the optimal system form the graph of embedded subalgebras. There are many chains of subalgebras in the graph. We consider the chain of embedded subalgebras containing operators of space and time translation, the rotation and uniform extension of all independent variables for the models of the continuous medium mechanics. We choose concordant invariants for eac

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25

Li, Pinmeng. "PageRank-Enhanced Deep Graph Network for Green Logistics Impact Assessment." International Journal of Information Technologies and Systems Approach 18, no. 1 (2025): 1–20. https://doi.org/10.4018/ijitsa.372060.

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Globalization and sustainability pressures demand accurate evaluation of green logistics supply chains. However, traditional methods are hampered by diverse data and complex relationships that limit their accuracy. To resolve these problems, this paper proposes a novel model called the deep graph network with PageRank-based feature selection combined with the meta-path-based heterogeneous graph convolutional network. The proposed approach integrates diverse indicators from supply chain management, operations, and technology. First, the PageRank algorithm selects key features, reduces data dime

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26

Burgarth, Daniel. "Identifying combinatorially symmetric Hidden Markov Models." Electronic Journal of Linear Algebra 34 (February 21, 2018): 393–98. http://dx.doi.org/10.13001/1081-3810.3651.

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A sufficient criterion for the unique parameter identification of combinatorially symmetric Hidden Markov Models, based on the structure of their transition matrix, is provided. If the observed states of the chain form a zero forcing set of the graph of the Markov model, then it is uniquely identifiable and an explicit reconstruction method is given.

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27

Nicolussi, Federica, and Roberto Colombi. "Type II chain graph models for categorical data: A smooth subclass." Bernoulli 23, no. 2 (2017): 863–83. http://dx.doi.org/10.3150/15-bej762.

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28

Stefanini, Federico M. "Chain Graph Models to Elicit the Structure of a Bayesian Network." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/749150.

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Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described. The

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29

Levitz, Michael, Michael D. Perlman, and David Madigan. "Correction: Separation and completeness properties for AMP chain graph Markov models." Annals of Statistics 31, no. 1 (2003): 348. http://dx.doi.org/10.1214/aos/1046294468.

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30

Kumpulainen, Iiro, Sebastian Dalleiger, Jilles Vreeken, and Nikolaj Tatti. "From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11987–94. https://doi.org/10.1609/aaai.v39i11.33305.

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Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out if these graphs share blocks with similar connectivity structures? In this paper, we propose the shared stochastic block modeling (SSBM) problem, in which we model n graphs using SBMs that share parameters of s blocks. We show that fitting

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31

La Rocca, Luca, and Alberto Roverato. "Chain graph models: topological sorting of meta-arrows and efficient construction of $${\mathcal{B}}$$ -essential graphs." Statistical Methods and Applications 17, no. 1 (2007): 73–83. http://dx.doi.org/10.1007/s10260-007-0050-z.

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32

Niu, Tianyu, Heng Zhang, Xingyou Yan, and Qiang Miao. "Intricate Supply Chain Demand Forecasting Based on Graph Convolution Network." Sustainability 16, no. 21 (2024): 9608. http://dx.doi.org/10.3390/su16219608.

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Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain

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Chang, Serina, Zhiyin Lin, Benjamin Yan, et al. "Learning Production Functions for Supply Chains with Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 27878–86. https://doi.org/10.1609/aaai.v39i27.35004.

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The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture th

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34

Karanam, Malleswari, Lanka Krishnanand, Vijaya Kumar Manupati, Katarzyna Antosz, and Jose Machado. "Identification of the Critical Enablers for Perishable Food Supply Chain Using Deterministic Assessment Models." Applied Sciences 12, no. 9 (2022): 4503. http://dx.doi.org/10.3390/app12094503.

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Today’s perishable food supply chains must be resilient to handle volatile demands, environmental restrictions, and disruptions in order to meet customers’ requirements. The enablers of the perishable food supply chain have not yet been explored. In this paper, a bibliometric systematic literature review has been conducted to identify the articles related to the perishable food supply chain. Next, with these identified articles, a map is created with bibliographic data using Vosviewer network visualization software, and then the enablers were identified by conducting keyword co-occurrence anal

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35

Vasudevan, K., M. Cavers, and A. Ware. "Earthquake sequencing: chimera states with Kuramoto model dynamics on directed graphs." Nonlinear Processes in Geophysics 22, no. 5 (2015): 499–512. http://dx.doi.org/10.5194/npg-22-499-2015.

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Abstract. Earthquake sequencing studies allow us to investigate empirical relationships among spatio-temporal parameters describing the complexity of earthquake properties. We have recently studied the relevance of Markov chain models to draw information from global earthquake catalogues. In these studies, we considered directed graphs as graph theoretic representations of the Markov chain model and analyzed their properties. Here, we look at earthquake sequencing itself as a directed graph. In general, earthquakes are occurrences resulting from significant stress interactions among faults. As

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Wang, Cong, Fangyue Yu, Zaixu Zhang, and Jian Zhang. "Multiview Graph Learning for Small- and Medium-Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance." Complexity 2021 (February 13, 2021): 1–13. http://dx.doi.org/10.1155/2021/6670873.

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In recent years, supply chain finance (SCF) is exploited to solve the financing difficulties of small- and medium-sized enterprises (SMEs). SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods mix all indicators from different index systems. T

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Vasudevan, K., M. Cavers, and A. Ware. "Earthquake sequencing: Chimera states with Kuramoto model dynamics on directed graphs." Nonlinear Processes in Geophysics Discussions 2, no. 1 (2015): 361–98. http://dx.doi.org/10.5194/npgd-2-361-2015.

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Abstract. Earthquake sequencing studies allow us to investigate empirical relationships among spatio-temporal parameters describing the complexity of earthquake properties. We have recently studied the relevance of Markov chain models to draw information from global earthquake catalogues. In these studies, we considered directed graphs as graph theoretic representations of the Markov chain model, and analyzed their properties. Here, we look at earthquake sequencing itself as a directed graph. In general, earthquakes are occurrences resulting from significant stress-interactions among faults. A

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38

Tyler, Steve, Christophe Laforge, Adrien Guzzo, Adrien Nicolaï, Gia G. Maisuradze, and Patrick Senet. "Einstein Model of a Graph to Characterize Protein Folded/Unfolded States." Molecules 28, no. 18 (2023): 6659. http://dx.doi.org/10.3390/molecules28186659.

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The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show

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Xiao, Ya-Qi, Sun-Wei Li, and Zhen-Zhong Hu. "Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules." Applied Sciences 9, no. 11 (2019): 2204. http://dx.doi.org/10.3390/app9112204.

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In mechanical, electrical, and plumbing (MEP) systems, logic chains refer to the upstream and downstream connections between MEP components. Generating the logic chains of MEP systems can improve the efficiency of facility management (FM) activities, such as locating components and retrieving relevant maintenance information for prompt failure detection or for emergency responses. However, due to the amount of equipment and components in commercial MEP systems, manually creating such logic chains is tedious and fallible work. This paper proposes an approach to generate the logic chains of MEP

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40

Yu, Chao, Yang Zhou, and Xiaolong Cui. "A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data." Mathematics 11, no. 21 (2023): 4543. http://dx.doi.org/10.3390/math11214543.

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With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally

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41

Beaumont, Olivier, Julien Herrmann, Guillaume Pallez (Aupy), and Alena Shilova. "Optimal memory-aware backpropagation of deep join networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (2020): 20190049. http://dx.doi.org/10.1098/rsta.2019.0049.

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Deep learning training memory needs can prevent the user from considering large models and large batch sizes. In this work, we propose to use techniques from memory-aware scheduling and automatic differentiation (AD) to execute a backpropagation graph with a bounded memory requirement at the cost of extra recomputations. The case of a single homogeneous chain, i.e. the case of a network whose stages are all identical and form a chain, is well understood and optimal solutions have been proposed in the AD literature. The networks encountered in practice in the context of deep learning are much m

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42

DRTON, MATHIAS, and MICHAEL EICHLER. "Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property." Scandinavian Journal of Statistics 33, no. 2 (2006): 247–57. http://dx.doi.org/10.1111/j.1467-9469.2006.00482.x.

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43

Nicolussi, Federica, and Fulvia Mecatti. "A smooth subclass of graphical models for chain graph: towards measuring gender gaps." Quality & Quantity 50, no. 1 (2014): 27–41. http://dx.doi.org/10.1007/s11135-014-0135-3.

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44

Flaccadoro, Diana, Cristiano Cervellera, Giorgio Bosia, and Eva Riccomagno. "Modelling of Fault Detection and Diagnostics for Hybrid Bus Using Chain Graph Models." Quality and Reliability Engineering International 30, no. 7 (2014): 975–83. http://dx.doi.org/10.1002/qre.1691.

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45

Zhao, Bo, Jianhou Gan, and Hualong Wu. "Redefined Zagreb indices of Some Nano Structures." Applied Mathematics and Nonlinear Sciences 1, no. 1 (2016): 291–300. http://dx.doi.org/10.21042/amns.2016.1.00024.

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AbstractIn theoretical chemistry, the researchers use graph models to express the structure of molecular, and the Zagreb indices and redefined Zagreb indices defined on molecular graph G are applied to measure the chemical characteristics of compounds and drugs. In this paper, we present the exact expressions of redefined Zagreb indices for certain important chemical structures like nanotube and nanostar. As supplement, the redefined Zagreb indices of polyomino chain and benzenoid series are manifested.

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Sburlan, Dragoş Florin, Cristina Sburlan, and Alexandru Bobe. "Tech Trend Analysis System: Using Large Language Models and Finite State Chain Machines." Electronics 14, no. 11 (2025): 2191. https://doi.org/10.3390/electronics14112191.

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In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and identify inflection points by themselves. In this paper, we aim to prove the potential of integrating large language models (LLMs) with a novel finite state chain machine (FSCM) with output and graph databases to extract insights from unstructured data, spec

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Zaourar, Lilia, Yann Kieffer, and Chouki Aktouf. "A Graph-Based Approach to Optimal Scan Chain Stitching Using RTL Design Descriptions." VLSI Design 2012 (December 20, 2012): 1–11. http://dx.doi.org/10.1155/2012/312808.

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The scan chain insertion problem is one of the mandatory logic insertion design tasks. The scanning of designs is a very efficient way of improving their testability. But it does impact size and performance, depending on the stitching ordering of the scan chain. In this paper, we propose a graph-based approach to a stitching algorithm for automatic and optimal scan chain insertion at the RTL. Our method is divided into two main steps. The first one builds graph models for inferring logical proximity information from the design, and then the second one uses classic approximation algorithms for

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Tandra, Achyut Kumar Sharma. "Advanced Graph Database Strategies: AI-Driven Migration and Security for Complex Relationships." International Journal of Computing and Engineering 7, no. 7 (2025): 39–52. https://doi.org/10.47941/ijce.2929.

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This article examines how graph database models address the fundamental drawbacks of traditional relational databases when handling highly interconnected datasets. By structuring data as nodes and relationships rather than tables that require expensive join operations, graph databases enable the rapid traversal and querying of complex relationship patterns. The article explores the theoretical foundations, architectural components, and performance characteristics that make graph databases particularly well-suited for applications in social networks, fraud detection, recommendation systems, and

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Besta, Maciej, Nils Blach, Ales Kubicek, et al. "Graph of Thoughts: Solving Elaborate Problems with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17682–90. http://dx.doi.org/10.1609/aaai.v38i16.29720.

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We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information ("LLM thoughts") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing th

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Lu, Yimin, Shiting Qiao, and Yiran Yao. "Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network." Sustainability 17, no. 1 (2025): 331. https://doi.org/10.3390/su17010331.

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Typhoon disasters not only trigger secondary disasters, such as rainstorms and flooding, but also bring many negative impacts on the normal operation of urban infrastructure and the safety of people’s lives and property. In order to effectively prevent the risks of typhoon disaster chain, this paper proposes a joint entity and relation extraction model based on RoBERTa-Adv-GPLinker. Then, relying on the ontology theory and methodology, we establish a knowledge graph of typhoon disaster chain. The results show that the joint extraction model based on RoBERTa-Adv-GPLinker outperforms other basel

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