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Journal articles on the topic 'Elemental graph data model'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Oganesyants, Lev, Alexandr Panasyuk, Elena Kuzmina, Dmitriy Sviridov, and Alexandr Ilyin. "Analyzing Geographical Origin of Grapes and Wines of Russia." BIO Web of Conferences 39 (2021): 06003. http://dx.doi.org/10.1051/bioconf/20213906003.

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In connection with the growing consumer’s interest to Russian wines with controlled place of origin PGI and PDO, the most pressing issue is the method of their identification. One of the most effective ways to confirm the wine's place of origin in world practice is a comprehensive research of the elemental profile and isotopic characteristics of “light” elements using the methods of statistical analysis. We have selected 32 samples of fresh grapes from various wine regions of Russia (Krasnodar Territory, Republic of Crimea, Republic of Dagestan). The grape must obtained from them was fermented

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Osadchuk, O. V., V. V. Martyniuk, M. V. Evseeva, and I. O. Osadchuk. "FREQUENCY TRANSDUCER OF MAGNETIC FIELD INDUCTION BASED ON HETEROMETALLIC COMPLEX COMPOUND." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 56–64. http://dx.doi.org/10.36994/2707-4110-2020-1-28-05.

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The possibilities of using nanocomposite material µ-methoxo (copper (II), bismuth (III)) acetylacetonate (I), the following composition: Cu3Bi(AA)4(OCH3)5, де HAA = H3C–C(O)–CH2–C(O)–CH3, as a magnetoresistive sensitive element, in a frequency transducer of a magnetic field. In order to create a suitable heterometallic complex compound, a method for its synthesis was developed. The structure, composition and physicochemical properties of the synthesized nanocomposite material were confirmed on the basis of elemental, X-ray phase analyzes, magnetochemical, IR spectroscopic and thermogravimetric

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Li, Na, Yu-Tao Liu, and Zhan Chen. "Unlocking insights: integrated text mining and interpretive structural modeling for enhanced user review analysis." PeerJ Computer Science 10 (December 23, 2024): e2541. https://doi.org/10.7717/peerj-cs.2541.

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Effective keywords are extracted from the massive milk product user review data to construct thematic terms and explore the elemental influence relationships to assist manufacturers, and e-commerce platforms in understanding user behaviour and preferences and further optimise product design and marketing strategies. By fusing two different text mining methods, term frequency-inverse document frequency (TF-IDF) and Word2vec, we explore the semantic relationships, then visualise the relevance of user reviews by drawing knowledge graphs with Neo4j, and finally, be able to explore the relationship

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Aslamiah, Aslamiah, Wahdah Refia Rafianti, Celia Cinantya, and Rizky Amelia. "Googling Model based on Interactive Multimedia for Elementary School Students." International Journal of Social Science And Human Research 05, no. 10 (2022): 4678–87. http://dx.doi.org/10.47191/ijsshr/v5-i10-37.

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This study aims to develop an interactive multimedia-based Googling learning model for elementary school students in Banjarmasin City. The research method used is Research and Development (R&D). The subject of this study was a Grade IV student at SD Surgi Mufti, Banjarmasin. Data collection techniques through questionnaires, observations, and tests. The data analysis technique used is a descriptive analysis using tables and graphs. The feasibility testing results of this interactive multimedia-based googling learning model product are categorized as feasible because the effects of media va

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Diomande, Bre Moussa, and Luc Laperrière. "AUTOMATIC LIAISON MODEL GENERATION FROM 3-D SOLID MODELS." Transactions of the Canadian Society for Mechanical Engineering 20, no. 4 (1996): 333–47. http://dx.doi.org/10.1139/tcsme-1996-0019.

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Computer Aided Assembly Planning (CAAP) is characterized by the manual generation of a liaison (or graph) model used to represent explicitly mating information among parts. This paper describes an automatic method for generating such relational information. It is based on an exhaustive and systematic surface-level analysis of the Boundary Representation (B-Rep) file of the solid modeled product. Simple mathematical tests performed on pairs of surfaces each on a different part enable the identification of mating surfaces. The system which performs this analysis also enables a visual display of

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Rukayah, Rukayah, Achmad Tolla, and Ramly Ramly. "The Development of Writing Poetry Teaching Materials Based on Audiovisual Media of Fifth Grade Elementary School in Bone Regency." Journal of Language Teaching and Research 9, no. 2 (2018): 358. http://dx.doi.org/10.17507/jltr.0902.18.

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The purpose of this research is to create the teaching material prototype which is valid, practical, and effective used in learning, especially in writing poetry of fifth grade elementary school in Bone regency. This study uses research and development method. The development model used is the Four-D models that include the definition (define), design, development (develop), and dissemination (disseminate). The design measures used in research and development (R&D) is pretest-posttest design. The data collection techniques used are observation, questionnaires, interviews, tests, and docume

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Perišić, Ana, Marko Lazić, and Ines Perišić. "Assessment of Building Air Quality in Respect of Eight Different Urban Block Designs Based on CFD Simulations." Applied Sciences 13, no. 13 (2023): 7408. http://dx.doi.org/10.3390/app13137408.

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Different urban block morphologies can greatly influence the air quality inside the buildings of the block. The model presented in this paper determines the correlation between block morphology and air quality, and outputs the indoor air quality via computational fluid dynamics (CFD) simulations. In this study, stagnant air was assumed to have a velocity lower than 0.15 m/s and considered to be low-quality air in the context of human health. The geometry of the urban blocks was simplified based on real-life buildings. Doors and windows were not 3D-modeled, and all the vertical surfaces of the

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Aslamiah, Refia Rafianti Wahdah, Cinantya Celia, and Amelia Rizky. "Googling Model based on Interactive Multimedia for Elementary School Students." International Journal of Social Science And Human Research 05, no. 10 (2022): 4678–87. https://doi.org/10.5281/zenodo.7256246.

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This study aims to develop an interactive multimedia-based Googling learning model for elementary school students in Banjarmasin City. The research method used is Research and Development (R&D). The subject of this study was a Grade IV student at SD Surgi Mufti, Banjarmasin. Data collection techniques through questionnaires, observations, and tests. The data analysis technique used is a descriptive analysis using tables and graphs. The feasibility testing results of this interactive multimedia-based googling learning model product are categorized as feasible because the effects of media va

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Kang, Mengjia, Jose A. Alvarado-Guzman, Luke V. Rasmussen, and Justin B. Starren. "Evolution of a Graph Model for the OMOP Common Data Model." Applied Clinical Informatics 15, no. 05 (2024): 1056–65. https://doi.org/10.1055/s-0044-1791487.

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Abstract Objective Graph databases for electronic health record (EHR) data have become a useful tool for clinical research in recent years, but there is a lack of published methods to transform relational databases to a graph database schema. We developed a graph model for the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that can be reused across research institutions. Methods We created and evaluated four models, representing two different strategies, for converting the standardized clinical and vocabulary tables of OMOP into a property graph model within the Neo4

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Radiansyah, Jannah Fathul, Ria Safitri Baharas Vita, Sari Raihanah, and Fahlevi Reja. "PANTING Model in Improving Critical Thinking Skills of Elementary School Students." International Journal of Social Science and Human Research 07, no. 07 (2024): 5523–28. https://doi.org/10.5281/zenodo.12889764.

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The problems in this study are critical thinking skills and student learning outcomes. This is because lacking training students' critical thinking skills to solve problems. Efforts are made to overcome this problem by using the PANTING model. This study aims to describe critical thinking skills and learning outcomes of students. This research is a Class Action Research (PTK) which is carried out with 4 meetings. The subjects of this study are students of class VB SDN Telawang 3 Banjarmasin, with a total of 21 students in the second semester of the 2023/2024 school year. The type of data used

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Sholeh, Muhammad, RR Yuliana Rachmawati, and Erma Susanti. "Pemodelan Basis data Graph dengan Neo4j (Studi Kasus : Basis Data Sistem Informasi Penjualan pada UMKM)." Jurnal Teknologi Informasi dan Terapan 7, no. 1 (2020): 25–32. http://dx.doi.org/10.25047/jtit.v7i1.129.

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Penelitian ini mengimplementasikan penyimpanan data dengan menggunakan basis data graph. Basis data graph merupakan salah satu kategori dari basis data noSQL. Dalam basis data model SQL data dibentuk dalam tabel –tabel yang terdiri dari baris dan kolom, sedangkan pada basis data model NoSQL data tidak memiliki skema standar yang harus didefinisikan. NoSql merupakan sistem manajemen basis data yang tidak mempunyai atau mematuhi aturan tertentu seperti pada model sistem manajemen basis data relasional. NoSQL memiliki skema yang dinamis sedangkan pada database SQL mengikuti skema yang telah ditet

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Wen, Lingfeng, Xuan Tang, Mingjie Ouyang, et al. "Hyperbolic Graph Diffusion Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15823–31. http://dx.doi.org/10.1609/aaai.v38i14.29512.

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Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structu

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Ding, Kaize, Zhe Xu, Hanghang Tong, and Huan Liu. "Data Augmentation for Deep Graph Learning." ACM SIGKDD Explorations Newsletter 24, no. 2 (2022): 61–77. http://dx.doi.org/10.1145/3575637.3575646.

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Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional data augmentation methods can hardly handle graph-structured data which is defined in non-Euclidean space with multi-modality. In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in

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Miyazaki, Tomo, and Shinichiro Omachi. "Structural Data Recognition With Graph Model Boosting." IEEE Access 6 (2018): 63606–18. http://dx.doi.org/10.1109/access.2018.2876860.

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Feigenbaum, Joan, Sampath Kannan, Andrew McGregor, Siddharth Suri, and Jian Zhang. "Graph Distances in the Data-Stream Model." SIAM Journal on Computing 38, no. 5 (2009): 1709–27. http://dx.doi.org/10.1137/070683155.

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Liu, Jian, Dong Chen, Jingyan Li, and Jie Wu. "Neighborhood hypergraph model for topological data analysis." Computational and Mathematical Biophysics 10, no. 1 (2022): 262–80. http://dx.doi.org/10.1515/cmb-2022-0142.

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Abstract Hypergraph, as a generalization of the notions of graph and simplicial complex, has gained a lot of attention in many fields. It is a relatively new mathematical model to describe the high-dimensional structure and geometric shapes of data sets. In this paper,we introduce the neighborhood hypergraph model for graphs and combine the neighborhood hypergraph model with the persistent (embedded) homology of hypergraphs. Given a graph,we can obtain a neighborhood complex introduced by L. Lovász and a filtration of hypergraphs parameterized by aweight function on the power set of the vertex

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Xu, Yuexuan, Jianyang Gao, Yutong Gou, Cheng Long, and Christian S. Jensen. "iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–26. https://doi.org/10.1145/3698814.

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Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a numeric range as parameters returns the data object whose numeric value is in the query range and whose vector is nearest to the query vector. To process this query, a recent study proposes to build O(n 2 ) dedicated graph-based indexes for all possible query ranges to enable efficient processing on a database of n objects. As storing all these

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Shen, Zhihong, Chuan Hu, and Zihao Zhao. "Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3926–29. http://dx.doi.org/10.14778/3611540.3611587.

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Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query fra

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Huang, Zhenhua, Yinhao Tang, and Yunwen Chen. "A graph neural network-based node classification model on class-imbalanced graph data." Knowledge-Based Systems 244 (May 2022): 108538. http://dx.doi.org/10.1016/j.knosys.2022.108538.

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Ding, Hongfa, Tian Tian, and Shiyun He. "Generative Graph based Model Inversion Attack on Graph Neural Network." International Journal of Applied Science 8, no. 2 (2025): p117. https://doi.org/10.30560/ijas.v8n2p117.

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Aiming at the privacy leakage risks of Graph Neural Networks (GNNs) in black-box scenarios, this paper proposes a Generation-Graph based Model Inversion Attack on GNN (GenG-MIA). By constructing a generative attack framework and integrating public knowledge distillation with structural optimization strategies, the proposed method effectively addresses challenges such as the high-dimensional sparsity of graph structure data, generative bias, and model collapse. GenG-MIA operates in two stages: first, during the public knowledge distillation stage, Wasserstein GAN is employed to train generators

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Janićijević, Stefana, Anđelija Mihajlović, Anđela Kojanić, and Aleksa Ćuk. "New approach for graph based data mining model." Serbian Journal of Engineering Management 7, no. 1 (2022): 1–12. http://dx.doi.org/10.5937/sjem2201001j.

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This paper presents new approach of Communication Network Analysis (CNA) that is interdisciplinary subfield of advanced concept of important Social Network Analysis (SNA). Objects in CNA are members of network discovered as vertices that are linked by edges. Identification of relevant vertices within connected components in telecommunication network graphs, such as influencers are proposed. Beside this result, the algorithm describes behaviour between component members, research interactions between components and telecom services usage. Algorithm is based on a combination of two important mac

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Chithambarathanu, M., and D. R. Ganesh. "Data Clustering using Genomic Analysis in Graph model." Journal of Physics: Conference Series 2161, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2161/1/012029.

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Abstract In the event that the data is addressed as a diagram, wherein the hubs are devices and the hyperlinks establish associations among devices then a bunch might be defined as an associated perspective; i.e., a gathering of devices that are identified with each other, yet that don’t have any association with objects outside the gathering. Bunching is an essential test in the quality examination. This ponders monster impact genetic field. Thusly in the current system, the various genomic assessments are scattered in various dispersed structures. In our proposed work, we endeavour to develo

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Levene, M., and G. Loizou. "A graph-based data model and its ramifications." IEEE Transactions on Knowledge and Data Engineering 7, no. 5 (1995): 809–23. http://dx.doi.org/10.1109/69.469818.

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Tuijn, Chris, and Marc Gyssens. "CGOOD, a categorical graph-oriented object data model." Theoretical Computer Science 160, no. 1-2 (1996): 217–39. http://dx.doi.org/10.1016/0304-3975(95)00089-5.

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Dutta, Hiren. "Graph Based Data Governance Model for Real Time Data Ingestion." International Journal of Information Technology and Computer Science 8, no. 10 (2016): 56–62. http://dx.doi.org/10.5815/ijitcs.2016.10.07.

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Dutta, Hiren. "Graph based data governance model for real time data ingestion." CSI Transactions on ICT 3, no. 2-4 (2015): 119–25. http://dx.doi.org/10.1007/s40012-016-0079-y.

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Crisan, Marius. "A Neural Network Model for Phoneme Generation." Applied Mechanics and Materials 367 (August 2013): 478–83. http://dx.doi.org/10.4028/www.scientific.net/amm.367.478.

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The paper discusses the possibility of phonemes generation based on a recurrent neural network model. In each phoneme a typical or elemental pattern can be identified that repeats itself with slight fluctuations along the signal length. This elemental pattern constitutes the training data for the recurrent neural network. After training, the network can generate three new periods of elemental patterns. In a repetitive loop the network can generate the entire phoneme signal. The model proved very simple and effective, and the generated phonemes gave the impression of a natural sound.

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Tuteja, Sonal, and Rajeev Kumar. "A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce." Data Science and Engineering 7, no. 1 (2021): 57–70. http://dx.doi.org/10.1007/s41019-021-00174-0.

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AbstractThe incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address t

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Pu, Shilin, Liang Chu, Jincheng Hu, Shibo Li, Jihao Li, and Wen Sun. "SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction." Sensors 22, no. 22 (2022): 9024. http://dx.doi.org/10.3390/s22229024.

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Accurate traffic prediction is significant in intelligent cities’ safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced.

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Pawlak, Zdzisław. "Data analysis and flow graphs." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2004): 1–5. http://dx.doi.org/10.26636/jtit.2004.3.255.

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In this paper we present a new approach to data analysis based on flow distribution study in a flow network. Branches of the flow graph are interpreted as decision rules, whereas the flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in the graph.

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Ye, Nanjun. "A Penetrative Multidimensional Data Analytics Model for Complex Relationship Mining over Knowledge Graphs." Journal of Computing and Electronic Information Management 17, no. 2 (2025): 34–41. https://doi.org/10.54097/87rgwp44.

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This study proposes a deep multidimensional data analytics framework for extracting intricate relationships from knowledge graphs, which tackles the challenge of discovering hidden connections in heterogeneous and high-dimensional datasets. The proposed method unifies three principal elements: Dynamic Meta-Path Penetration, Nested Subgraph Extraction, and Tensor-Graph Fusion, which together permit a structured investigation of hidden connections. Dynamic Meta-Path Penetration applies reinforcement learning to traverse the graph, directed by a reward system prioritizing informative routes. Nest

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PYSMENNYI, Ihor, Anatolii PETRENKO, and Roman KYSLYI. "GRAPH-BASED FOG COMPUTING NETWORK MODEL." Applied Computer Science 16, no. 4 (2020): 5–20. http://dx.doi.org/10.35784/acs-2020-25.

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IoT networks generate numerous amounts of data that is then transferred to the cloud for processing. Transferring data cleansing and parts of calculations towards these edge-level networks improves system’s, latency, energy consumption, network bandwidth and computational resources utilization, fault tolerance and thus operational costs. On the other hand, these fog nodes are resource-constrained, have extremely distributed and heterogeneous nature, lack horizontal scalability, and, thus, the vanilla SOA approach is not applicable to them. Utilization of Software Defined Network (SDN) with tas

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Lin, Zhenxian, Jiagang Wang, and Chengmao Wu. "Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model." Axioms 11, no. 6 (2022): 269. http://dx.doi.org/10.3390/axioms11060269.

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Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the inherent structural information between data points. This study uses graph convolutional subspace clustering (GCSC) for robust HSI clustering. The model remaps the self-expression of the data to non-Euclidean domains, which can generate a robust graph embedding dictionary. The EKGCSC model can achieve a globally optimal closed-form solution by

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IWASAKI, S., H. FUKUDA, K. YOSHIZAKI, et al. "ELEMENTAL RESPONSES FOR NEURAL-NETWORK ANALYSIS OF PIXE SPECTRA." International Journal of PIXE 05, no. 02n03 (1995): 175–79. http://dx.doi.org/10.1142/s0129083595000216.

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A semi-empirical approach of elemental response functions for linear associated neural network analysis is proposed. The proposed method is a combined one of both the pure experimental and model based ones: spectra of standard single elemental samples of selected elements are measured, and model parameters related to the analyzing system are deduced: elemental responses are calculated by a suitable peak shape model and corrected with respect to the efficiency: channel-wise response data are generated using the Monte-Carlo simulation: finally, the response data are adjusted to the corresponding

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Hammer, Florian, and Sarah Barber. "Data imputation for SCADA data using Graph Neural Networks." Journal of Physics: Conference Series 3025, no. 1 (2025): 012014. https://doi.org/10.1088/1742-6596/3025/1/012014.

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Abstract Missing data in wind turbine SCADA systems can arise due to sensor failures, software issues, or maintenance, impacting data mining and analysis tasks. This work investigates the use of Graph Neural Networks (GNNs) for imputing missing wind speed data by leveraging global and local spatial relationships between turbines. The goal is to improve data quality and completeness for downstream tasks such as energy loss estimations and performance analysis. A GNN model was developed using SCADA data from the Kelmarsh wind farm, incorporating wind speed components and turbine nacelle orientat

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Buneman, Peter, Dennis Dosso, Matteo Lissandrini, and Gianmaria Silvello. "Data citation and the citation graph." Quantitative Science Studies 2, no. 4 (2021): 1399–422. http://dx.doi.org/10.1162/qss_a_00166.

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Abstract The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the

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Gammanee, Sutat, and Sunantha Sodsee. "Selecting the appropriate size ofthe graph for self-diagnostic model with graph density." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1556–63. https://doi.org/10.11591/ijeecs.v26.i3.pp1556-1563.

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Self-diagnosis is the concept of self-diagnosing disease from symptoms. We have the idea to create self-diagnostic models from diagnostic data. The data to be analyzed were from a medium-sized hospital in Thailand. The model is divided by structured data and unstructured data. The first step is to process structured data with cluster algorithms. The second step is to evaluate the unstructured data to group symptoms into a bipartite graph. After the graph was created, the model was divided into 10 levels, according to the level of similarity. This research aims to apply the concept of density g

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Lisin, V. A., A. S. Sery, and E. A. Sidorova. "Domain Ontology Representation Model Based on Graph Databases." Vestnik NSU. Series: Information Technologies 20, no. 4 (2023): 24–38. http://dx.doi.org/10.25205/1818-7900-2022-20-4-24-38.

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The article presents an approach to modeling domain ontologies based on graph databases. Ontology is traditionally considered as a means of studying and formalizing the subject area. Based on ontologies, knowledge bases of information systems are formed, which can later be replenished and used to study certain applied aspects of the subject area. At the same time, with the development of NoSQL technologies and graph databases aimed at optimizing work with related data, it becomes possible to design a data warehouse without a strict pre-established domain model. Due to the obvious graph nature

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Chen, Huasheng, Chao Liu, and Xiaoxiao Xu. "Molecular dynamic simulation of sulfur solubility in H2S system." International Journal of Modern Physics B 33, no. 08 (2019): 1950052. http://dx.doi.org/10.1142/s0217979219500528.

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The elemental sulfur solubility in sour gas plays an important role in H2S-rich gas reservoir development and transportation. While the solubility of elemental sulfur in sour gas can be measured in macroscopical respect, the interaction of solid deposition is not clear at microscale. In this work, molecular dynamic simulation (MD) was adopted to predict the solubility of elemental sulfur in hydrogen sulfide at nanoscale. It is found that the results of new nanoscale solubility model are close to the reported experimental data. The average relative error of the solubility of elemental sulfur in

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Yao, Yang, Xin Wang, Yijian Qin, Ziwei Zhang, Wenwu Zhu, and Hong Mei. "Data-Augmented Curriculum Graph Neural Architecture Search under Distribution Shifts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16433–41. http://dx.doi.org/10.1609/aaai.v38i15.29580.

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Graph neural architecture search (NAS) has achieved great success in designing architectures for graph data processing.However, distribution shifts pose great challenges for graph NAS, since the optimal searched architectures for the training graph data may fail to generalize to the unseen test graph data. The sole prior work tackles this problem by customizing architectures for each graph instance through learning graph structural information, but failed to consider data augmentation during training, which has been proven by existing works to be able to improve generalization.In this paper, w

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Li, Shujie, Liang Li, Ruiying Geng, et al. "Unifying Structured Data as Graph for Data-to-Text Pre-Training." Transactions of the Association for Computational Linguistics 12 (2024): 210–28. http://dx.doi.org/10.1162/tacl_a_00641.

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Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2

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Javaherian Pour, Ensiyeh, Behnam Atazadeh, Abbas Rajabifard, and Soheil Sabri. "Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 10, 2025): 405–12. https://doi.org/10.5194/isprs-annals-x-g-2025-405-2025.

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Abstract. Graph data models are essential for the development of smart cities, where interconnected systems such as utility networks, transportation, and IoT devices must function cohesively. The complexity of smart city infrastructure necessitates 3D data structures capable of managing intricate relationships, dynamic environments, and high connectivity across diverse systems. Graph data models are particularly suited for this purpose, as they offer an integrated 3D digital representation of urban complexity and interconnectivity. This study employs the Labelled Property Graph (LPG) framework

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Pasham, Sai Dikshit. "Integrating Big Data with Graph Theory to Model Complex Transportation Systems." Research and Analysis Journal 5, no. 6 (2022): 01–27. https://doi.org/10.18535/raj.v5i6.294.

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The rapid growth of urbanization and globalization has amplified the complexity of modern transportation systems, necessitating innovative approaches to model and optimize these networks. This study explores the integration of Big Data and graph theory as a robust framework for analyzing and managing complex transportation systems. By leveraging vast datasets sourced from IoT devices, GPS trackers, and social media, the research harnesses the power of graph-based models to represent transportation networks with high fidelity. Key methodologies include the application of dynamic graph algorithm

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Wang, Xiaochen. "Developing Multimodal Healthcare Foundation Model: From Data-driven to Knowledge-enhanced." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29305–6. https://doi.org/10.1609/aaai.v39i28.35230.

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Foundation models in general domains have leveraged multimodal knowledge graphs to great effect, yet the healthcare sector lacks such comprehensive structures, presenting a significant gap in current research. Based on previous exploration with pure data-driven approaches, this proposal describes a two-stage project aiming to enhance multimodal healthcare foundation model with domain knowledge. The first stage is to construct a robust multimodal healthcare knowledge graph based on established healthcare taxonomies, such as UMLS, and enriched with data from multimodal clinical databases like MI

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Moon, Heekyung, Zhanfang Zhao, Jintak Choi, and Sungkook Han. "A novel property graph model for knowledge representation on the Web." International Journal of Engineering & Technology 7, no. 3.33 (2018): 187. http://dx.doi.org/10.14419/ijet.v7i3.33.21010.

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Graphs provide an effective way to represent information and knowledge of real world domains. Resource Description Framework (RDF) model and Labeled Property Graphs (LPG) model are dominant graph data models widely used in Linked Open Data (LOD) and NoSQL databases. Although these graph models have plentiful data modeling capabilities, they reveal some drawbacks to model the complicated structures. This paper proposes a new property graph model called a universal property graph (UPG) that can embrace the capability of both RDF and LPG. This paper explores the core features of UPG and their fun

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Fan, Shixiong, Xingwei Liu, Ying Chen, et al. "How to Construct a Power Knowledge Graph with Dispatching Data?" Scientific Programming 2020 (July 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8842463.

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Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of powe

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Selvaraj, Devipriya, Vijaya M S, and Krishnaveni Sakkarapani. "Improved half-maximal inhibitory concentration regression model using amyotrophic lateral sclerosis data." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2276–87. https://doi.org/10.11591/eei.v14i3.8520.

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The current research addresses the critical need for precise half-maximal inhibitory concentration regression in the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Unavailable drug-induced gene expressions and irrelevant molecular descriptors have yielded regression models with less accuracy using traditional machine learning (ML). Drugs can be converted to graph format and integrated with gene expressions to learn drug-gene interactions better thereby producing precise half-maximal inhibitory concentration regression models. To accomplish this, three variants of graph neural

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Li, Xue, Weibin Zeng, Zhibin Wang, et al. "GraphAr: An Efficient Storage Scheme for Graph Data in Data Lakes." Proceedings of the VLDB Endowment 18, no. 3 (2024): 530–43. https://doi.org/10.14778/3712221.3712223.

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Data lakes, increasingly adopted for their ability to store and analyze diverse types of data, commonly use columnar storage formats like Parquet and ORC for handling relational tables. However, these traditional setups fall short when it comes to efficiently managing graph data, particularly those conforming to the Labeled Property Graph (LPG) model. To address this gap, this paper introduces GraphAr, a specialized storage scheme designed to enhance existing data lakes for efficient graph data management. Leveraging the strengths of Parquet, GraphAr captures LPG semantics precisely and facili

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Gabriel, Nicholas, and Neil F. Johnson. "Using Neural Architectures to Model Complex Dynamical Systems." Advances in Artificial Intelligence and Machine Learning 02, no. 02 (2022): 366–84. http://dx.doi.org/10.54364/aaiml.2022.1124.

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The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable successin leveraging large amounts of unstruc

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Noda, Atsushi, Hideitsu Hino, Masami Tatsuno, Shotaro Akaho, and Noboru Murata. "Intrinsic Graph Structure Estimation Using Graph Laplacian." Neural Computation 26, no. 7 (2014): 1455–83. http://dx.doi.org/10.1162/neco_a_00603.

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A graph is a mathematical representation of a set of variables where some pairs of the variables are connected by edges. Common examples of graphs are railroads, the Internet, and neural networks. It is both theoretically and practically important to estimate the intensity of direct connections between variables. In this study, a problem of estimating the intrinsic graph structure from observed data is considered. The observed data in this study are a matrix with elements representing dependency between nodes in the graph. The dependency represents more than direct connections because it inclu

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