Relevant bibliographies by topics / Graph transformer

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph transformer'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Graph transformer"

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Nguyen, Hoang D., Xuan-Son Vu, and Duc-Trong Le. "Modular Graph Transformer Networks for Multi-Label Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 9092–100. http://dx.doi.org/10.1609/aaai.v35i10.17098.

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With the recent advances in graph neural networks, there is a rising number of studies on graph-based multi-label classification with the consideration of object dependencies within visual data. Nevertheless, graph representations can become indistinguishable due to the complex nature of label relationships. We propose a multi-label image classification framework based on graph transformer networks to fully exploit inter-label interactions. The paper presents a modular learning scheme to enhance the classification performance by segregating the computational graph into multiple sub-graphs base
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Lou, Wei, Guanbin Li, Xiang Wan, and Haofeng Li. "Cell Graph Transformer for Nuclei Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3873–81. http://dx.doi.org/10.1609/aaai.v38i4.28179.

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Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell
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Oumaima, Hourrane, and Habib Benlahmar El. "Graph transformer for cross-lingual plagiarism detection." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 905–15. https://doi.org/10.11591/ijai.v11.i3.pp905-915.

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The existence of vast amounts of multilingual textual data on the internet leads to cross-lingual plagiarism which becomes a serious issue in different fields such as education, science, and literature. Current cross-lingual plagiarism detection approaches usually employ syntactic and lexical properties, external machine translation systems, or finding similarities within a multilingual set of text documents. However, most of these methods are conceived for literal plagiarism such as copy and paste, and their performance is diminished when handling complex cases of plagiarism including paraphr
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Hou, Guoqiang, Qiwen Yu, Fan Chen, and Guang Chen. "Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs." Mathematics 12, no. 23 (2024): 3689. http://dx.doi.org/10.3390/math12233689.

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Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature space. The directed graph is a general representation of unstructured knowledge graphs. However, existing methods lack the ability to simultaneously approximate high-order filters and globally pay attention to the task-related connectivity between distant nodes for directed graphs. To address this limitation, a directed spectral graph transformer (D
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AlBadani, Barakat, Ronghua Shi, Jian Dong, Raeed Al-Sabri, and Oloulade Babatounde Moctard. "Transformer-Based Graph Convolutional Network for Sentiment Analysis." Applied Sciences 12, no. 3 (2022): 1316. http://dx.doi.org/10.3390/app12031316.

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Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models canno
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Zhou, Zhe-Wei, Wen-Ren Jong, Yu-Hung Ting, Shia-Chung Chen, and Ming-Chien Chiu. "Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT." Applied Sciences 13, no. 11 (2023): 6687. http://dx.doi.org/10.3390/app13116687.

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Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage and express relevant knowledge, gradually establishing an injection molding industrial knowledge graph. However, the current way of retrieving knowledge graphs is still mainly through programming, which results in many difficulties for users without programming backgrounds when it comes to searching a graph. Thi
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Hourrane, Oumaima, and El Habib Benlahmar. "Graph transformer for cross-lingual plagiarism detection." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 905. http://dx.doi.org/10.11591/ijai.v11.i3.pp905-915.

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<span lang="EN-US">The existence of vast amounts of multilingual textual data on the internet leads to cross-lingual plagiarism which becomes a serious issue in different fields such as education, science, and literature. Current cross-lingual plagiarism detection approaches usually employ syntactic and lexical properties, external machine translation systems, or finding similarities within a multilingual set of text documents. However, most of these methods are conceived for literal plagiarism such as copy and paste, and their performance is diminished when handling complex cases of pla
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Vaghani, Dev. "An Approch for Representation of Node Using Graph Transformer Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 27–37. http://dx.doi.org/10.22214/ijraset.2023.48485.

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Abstract: In representation learning on graphs, graph neural networks (GNNs) have been widely employed and have attained cutting-edge performance in tasks like node categorization and link prediction. However, the majority of GNNs now in use are made to learn node representations on homogenous and fixed graphs. The limits are particularly significant when learning representations on a network that has been incorrectly described or one that is heterogeneous, or made up of different kinds of nodes and edges. This study proposes Graph Transformer Networks (GTNs), which may generate new network st
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Wang, Tianming, Xiaojun Wan, and Hanqi Jin. "AMR-To-Text Generation with Graph Transformer." Transactions of the Association for Computational Linguistics 8 (July 2020): 19–33. http://dx.doi.org/10.1162/tacl_a_00297.

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Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the previous sequence-to-sequence models or statistical approaches. In this paper, we propose a novel graph-to-sequence model (Graph Transformer) to address this task. The model directly encodes the AMR graphs and learns the node representations. A pairwise interaction function is used
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Liu, Xiangwen, Shengyu Mao, Xiaohan Wang, and Jiajun Bu. "Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph Completion." Mathematics 11, no. 5 (2023): 1073. http://dx.doi.org/10.3390/math11051073.

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Academic knowledge graphs are essential resources and can be beneficial in widespread real-world applications. Most of the existing academic knowledge graphs are far from completion; thus, knowledge graph completion—the task of extending a knowledge graph with missing entities and relations—attracts many researchers. Most existing methods utilize low-dimensional embeddings to represent entities and relations and follow the discrimination paradigm for link prediction. However, discrimination approaches may suffer from the scaling issue during inference with large-scale academic knowledge graphs
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Dissertations / Theses on the topic "Graph transformer"

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Sandström, Emil. "Molecular Optimization Using Graph-to-Graph Translation." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172584.

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Drug development is a protracted and expensive process. One of the main challenges indrug discovery is to find molecules with desirable properties. Molecular optimization is thetask of optimizing precursor molecules by affording them with desirable properties. Recentadvancement in Artificial Intelligence, has led to deep learning models designed for molecularoptimization. These models, that generates new molecules with desirable properties, have thepotential to accelerate the drug discovery. In this thesis, I evaluate the current state-of-the-art graph-to-graph translation model formolecular o
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Holmström, Oskar. "Exploring Transformer-Based Contextual Knowledge Graph Embeddings : How the Design of the Attention Mask and the Input Structure Affect Learning in Transformer Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175400.

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The availability and use of knowledge graphs have become commonplace as a compact storage of information and for lookup of facts. However, the discrete representation makes the knowledge graph unavailable for tasks that need a continuous representation, such as predicting relationships between entities, where the most probable relationship needs to be found. The need for a continuous representation has spurred the development of knowledge graph embeddings. The idea is to position the entities of the graph relative to each other in a continuous low-dimensional vector space, so that their relati
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Dronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.

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This thesis deals with rotation estimation of 3D model of human jaw. It describes and compares methods for direct analysis od 3D models as well as method to analyze model using rasterization. To evaluate perfomance of proposed method, a metric that computes number of cases when prediction was less than 30° from ground truth is used. Proposed method that uses rasterization, takes  three x-ray views of model as an input and processes it with convolutional network. It achieves best preformance, 99% with described metric. Method to directly analyze polygonal model as a sequence uses attention mech
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Balzani, Lorenzo. "Verbalizzazione di eventi biomedici espressi nella letteratura scientifica: generazione controllata di linguaggio naturale da grafi di conoscenza mediante transformer text-to-text." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24286/.

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Il periodo in cui viviamo rappresenta la cuspide di una forte e rapida evoluzione nella comprensione del linguaggio naturale, raggiuntasi prevalentemente grazie allo sviluppo di modelli neurali. Nell'ambito dell'information extraction, tali progressi hanno recentemente consentito di riconoscere efficacemente relazioni semantiche complesse tra entità menzionate nel testo, quali proteine, sintomi e farmaci. Tale task -- reso possibile dalla modellazione ad eventi -- è fondamentale in biomedicina, dove la crescita esponenziale del numero di pubblicazioni scientifiche accresce ulteriormente il bis
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Rizkallah, Mira. "Graph based transforms for compression of new imaging modalities." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S021/document.

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En raison de la grande disponibilité de nouveaux types de caméras capturant des informations géométriques supplémentaires, ainsi que de l'émergence de nouvelles modalités d'image telles que les champs de lumière et les images omnidirectionnelles, il est nécessaire de stocker et de diffuser une quantité énorme de hautes dimensions. Les exigences croissantes en matière de streaming et de stockage de ces nouvelles modalités d’image nécessitent de nouveaux outils de codage d’images exploitant la structure complexe de ces données. Cette thèse a pour but d'explorer de nouvelles approches basées sur
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Goodall, A. J. "Graph polynomials and the discrete Fourier transform." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401064.

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Zanella, Calzada Laura A. "Biomedical Event Extraction Based on Transformers and Knowledge Graphs." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0235.

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L'extraction d'événements biomédicaux peut être divisée en trois sous-tâches principales : (1) la détection de déclencheurs d'événements biomédicaux, (2) l'identification d'arguments biomédicaux et (3) la construction d'événements. Dans cette étude, pour la première sous-tâche, nous analysons un ensemble de modèles de langage transformer couramment utilisés dans le domaine biomédical afin d'évaluer et de comparer leur capacité à détecter les déclencheurs d'événements. Nous affinons les modèles en utilisant sept corpus annotés manuellement pour évaluer leurs performances dans différents sous-do
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Zhou, Bingxin. "Geometric Signal Processing with Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28617.

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One of the most predominant techniques that have achieved phenomenal success in many modern applications is deep learning. The obsession with massive data analysis in image recognition, speech processing, and text understanding spawns remarkable advances in deep learning of diverse research areas. The alliance of deep learning technologies yields mighty graph neural networks (GNNs), an emerging type of deep neural networks that encodes internal structural relationships of inputs. The mainstream of GNNs finds an adequate numerical representation of graphs, which is vital to the prediction perfo
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Malek, Mohamed. "Extension de l'analyse multi-résolution aux images couleurs par transformées sur graphes." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2304/document.

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Dans ce manuscrit, nous avons étudié l’extension de l’analyse multi-résolution aux images couleurs par des transformées sur graphe. Dans ce cadre, nous avons déployé trois stratégies d’analyse différentes. En premier lieu, nous avons défini une transformée basée sur l’utilisation d’un graphe perceptuel dans l’analyse à travers la transformé en ondelettes spectrale sur graphe. L’application en débruitage d’image met en évidence l’utilisation du SVH dans l’analyse des images couleurs. La deuxième stratégie consiste à proposer une nouvelle méthode d’inpainting pour des images couleurs. Pour cela,
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FRACASTORO, GIULIA. "Design and Optimization of Graph Transform for Image and Video Compression." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2671060.

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The main contribution of this thesis is the introduction of new methods for designing adaptive transforms for image and video compression. Exploiting graph signal processing techniques, we develop new graph construction methods targeted for image and video compression applications. In this way, we obtain a graph that is, at the same time, a good representation of the image and easy to transmit to the decoder. To do so, we investigate different research directions. First, we propose a new method for graph construction that employs innovative edge metrics, quantization and edge prediction techni
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Books on the topic "Graph transformer"

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Seslavin, Andrey. Theory of automatic control. Linear, continuous systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1014654.

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The textbook presents the basics of the classical theory of automatic control, based on mathematical models of real systems, given in the form of systems of linear differential equations with constant coefficients. Methods based on Laplace and Fourier transforms, stability, controllability, and observability theory, as well as directed graph theory and linear algebra are used. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the areas of training and specialties 15.00.00 "
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Schurz, Henri, Philip J. Feinsilver, Gregory Budzban, and Harry Randolph Hughes. Probability on algebraic and geometric structures: International research conference in honor of Philip Feinsilver, Salah-Eldin A. Mohammed, and Arunava Mukherjea, June 5-7, 2014, Southern Illinois University, Carbondale, Illinois. Edited by Mohammed Salah-Eldin 1946- and Mukherjea Arunava 1941-. American Mathematical Society, 2016.

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Gelman, Andrew, and Deborah Nolan. Statistical graphics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0004.

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A statistical graph can offer an alternative compelling approach to teaching statistical thinking, but making good statistical graphs is hard to do. Each step in the process (e.g., change in scale, transform a variable, select colors, add a reference marker) engages students in better understanding data and models. However, this creative process is not easily encapsulated in a textbook. Since it is relatively easy to make a basic plot with statistical software, we can engage students in activities around making statistical graphs. This chapter provides guiding principles and lecture topics for
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Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Introduction to presenting statistical analyses. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0005.

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Chapter 5 discusses presenting statistical analyses, and covers numerical data, results section, describing the results, assessing non-response bias, presenting results for different media, and drawing up subject profiles, graphs and tables, and categorical and continuous data. It shows how to present data that have been transformed for analysis. It contains helpful tips and information about reporting descriptive data clearly. The chapter includes analyses using Stata, SAS, SPSS, and R.
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Bisseling, Rob H. Parallel Scientific Computation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198788348.001.0001.

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This book explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data. Furthermore, it presents a hybrid BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. The book provides a full treatment of core problems in scientific computing and big data, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in the communication library B
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Headrick, Daniel R. When Information Came of Age. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195135978.001.0001.

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Although the Information Age is often described as a new era, a cultural leap springing directly from the invention of modern computers, it is simply the latest step in a long cultural process. Its conceptual roots stretch back to the profound changes that occurred during the Age of Reason and Revolution. When Information Came of Age argues that the key to the present era lies in understanding the systems developed in the eighteenth and early nineteenth centuries to gather, store, transform, display, and communicate information. The book provides a concise and readable survey of the many conce
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Book chapters on the topic "Graph transformer"

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Chen, Ke, Xinyan He, Yong Zhong, and Cheng Peng. "GTA: Graph Transformer Adapter." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5672-8_21.

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Liu, Fan, and Liqiang Nie. "Light Graph Transformer Model." In Advancing Recommender Systems with Graph Convolutional Networks. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85093-6_6.

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Li, Jiaxing, Ke Zhang, Xinyan Pu, and Youyong Kong. "Graph Attention Mixup Transformer for Graph Classification." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1645-0_16.

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Wang, Boyuan, Lixin Cui, Lu Bai, and Edwin R. Hancock. "Graph Transformer: Learning Better Representations for Graph Neural Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73973-7_14.

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Li, Xiangyu, Sen Hu, and Lei Zou. "Natural Answer Generation via Graph Transformer." In Web and Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_23.

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Wang, Cheng. "Associations Dynamic Evolution: Evolving Graph Transformer." In Anti-Fraud Engineering for Digital Finance. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5257-1_8.

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Zhu, Jinghua, Yanchang Cui, Zhuohao Zhang, and Heran Xi. "Knowledge Graph Transformer for Sequential Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44223-0_37.

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Sakhovskiy, Andrey, Natalia Semenova, Artur Kadurin, and Elena Tutubalina. "Graph-Enriched Biomedical Entity Representation Transformer." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42448-9_10.

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Wang, Zehong, Qi Li, Donghua Yu, and Xiaolong Han. "Temporal Graph Transformer for Dynamic Network." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_57.

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Chen, Ke, XinYang He, Yong Zhong, and Cheng Peng. "Correction to: GTA: Graph Transformer Adapter." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5672-8_42.

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Conference papers on the topic "Graph transformer"

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Jia, Haohe, Yi Huang, Hongbin Zhu, and Hongfeng Chai. "Personalized Graph Transformer for Federated Graph Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889973.

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Deng, Zewen, Zan Zhang, Bingbing Dong, Xiulin Zheng, and Xindong Wu. "Feature Enhancement for Multi-modal Entity Alignment Transformer." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00014.

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Im, Jinbae, JeongYeon Nam, Nokyung Park, Hyungmin Lee, and Seunghyun Park. "EGTR: Extracting Graph from Transformer for Scene Graph Generation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02287.

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Kodama, Sohei, and Shin Ando. "GTFormer: A Geospatial Temporal Transformer for Crowd Flow Prediction." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00029.

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Dai, JiaXuan, LiangLi Ma, and JiWei Qin. "COM-transformers: research on automatic knowledge graph construction based on transformer." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3060407.

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Qin, Tianzuo, Junwei Su, and Chuan Wu. "HEGformer: Representation Learning on Heterogeneous Graph Neural Network with Efficient Transformer." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00041.

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Wang, Zhi-Jie, Naikang Zhong, and Xiao Lin. "A Novel Transformer-based Two Stage Framework for Multi-Label Image Classification." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00058.

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Kini, Venkataramana, Ravi Divvela, Unmesh Phadke, Narayanan Sadagopan, Fei Wang та Zhen Wen. "Trajectory Boosted Transformer Model and KG/LLM based μ-Genre for PV Offer/Content Type Arbitration". У 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00015.

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Zhang, Hu, Shanshan Yang, Guangjun Zhang, and Kunrui Li. "Substructure-augmented Graph Transformer for Network Representation Learning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650880.

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Zhu, Tong, Huobin Tan, XinYu Chen, and Yating Ren. "A Transformer-based Knowledge Graph Embedding Model Combining Graph Paths and Local Neighborhood." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650666.

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Reports on the topic "Graph transformer"

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Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, and Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317672.

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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a ch
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4

Reisch, Bruce, Avichai Perl, Julie Kikkert, Ruth Ben-Arie, and Rachel Gollop. Use of Anti-Fungal Gene Synergisms for Improved Foliar and Fruit Disease Tolerance in Transgenic Grapes. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7575292.bard.

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Abstract:
Original objectives . 1. Test anti-fungal gene products for activity against Uncinula necator, Aspergillus niger, Rhizopus stolonifer and Botrytis cinerea. 2. For Agrobacterium transformation, design appropriate vectors with gene combinations. 3. Use biolistic bombardment and Agrobacterium for transformation of important cultivars. 4. Characterize gene expression in transformants, as well as level of powdery mildew and Botrytis resistance in foliage of transformed plants. Background The production of new grape cultivars by conventional breeding is a complex and time-consuming process. Transfer
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Perl, Avichai, Bruce I. Reisch, and Ofra Lotan. Transgenic Endochitinase Producing Grapevine for the Improvement of Resistance to Powdery Mildew (Uncinula necator). United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568766.bard.

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The original objectives are listed below: 1. Design vectors for constitutive expression of endochitinase from Trichoderma harzianum strain P1. Design vectors with signal peptides to target gene expression. 2. Extend transformation/regeneration technology to other cultivars of importance in the U.S. and Israel. 3. Transform cultivars with the endochitinase constructs developed as part of objective 1. A. Characterize foliar powdery mildew resistance in transgenic plants. Background of the topic Conventional breeding of grapevines is a slow and imprecise process. The long generation cycle, large
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