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Статті в журналах з теми "Structural Graph Representations"
Zhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.
Повний текст джерелаMalaviya, Chaitanya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. "Commonsense Knowledge Base Completion with Structural and Semantic Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2925–33. http://dx.doi.org/10.1609/aaai.v34i03.5684.
Повний текст джерелаWang, Yifei, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, and Pengyu Hong. "Motif-Based Graph Representation Learning with Application to Chemical Molecules." Informatics 10, no. 1 (January 11, 2023): 8. http://dx.doi.org/10.3390/informatics10010008.
Повний текст джерелаJoaristi, Mikel, and Edoardo Serra. "SIR-GN: A Fast Structural Iterative Representation Learning Approach For Graph Nodes." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (May 19, 2021): 1–39. http://dx.doi.org/10.1145/3450315.
Повний текст джерелаLyu, Gengyu, Xiang Deng, Yanan Wu, and Songhe Feng. "Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7647–54. http://dx.doi.org/10.1609/aaai.v36i7.20731.
Повний текст джерелаLi, Wang, Siwei Wang, Xifeng Guo, Zhenyu Zhou, and En Zhu. "Auxiliary Graph for Attribute Graph Clustering." Entropy 24, no. 10 (October 2, 2022): 1409. http://dx.doi.org/10.3390/e24101409.
Повний текст джерелаLv, Shangwen, Daya Guo, Jingjing Xu, Duyu Tang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, and Songlin Hu. "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8449–56. http://dx.doi.org/10.1609/aaai.v34i05.6364.
Повний текст джерелаTa'aseh, Nevo, and Offer Shai. "Network Graph Theory Perspective on Skeletal Structures for Theoretical and Educational Purposes." International Journal of Mechanical Engineering Education 36, no. 4 (October 2008): 294–319. http://dx.doi.org/10.7227/ijmee.36.4.3.
Повний текст джерелаWang, Yu, Liang Hu, Yang Wu, and Wanfu Gao. "Graph Multihead Attention Pooling with Self-Supervised Learning." Entropy 24, no. 12 (November 29, 2022): 1745. http://dx.doi.org/10.3390/e24121745.
Повний текст джерелаYoon, Jisung, Kai-Cheng Yang, Woo-Sung Jung, and Yong-Yeol Ahn. "Persona2vec: a flexible multi-role representations learning framework for graphs." PeerJ Computer Science 7 (March 30, 2021): e439. http://dx.doi.org/10.7717/peerj-cs.439.
Повний текст джерелаДисертації з теми "Structural Graph Representations"
Gibert, Domingo Jaume. "Vector Space Embedding of Graphs via Statistics of Labelling Information." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96240.
Повний текст джерелаPattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyze patterns. Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding. In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
Sadeghi, Kayvan. "Graphical representation of independence structures." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:86ff6155-a6b9-48f9-9dac-1ab791748072.
Повний текст джерелаTsitsulin, Anton [Verfasser]. "Similarities and Representations of Graph Structures / Anton Tsitsulin." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1238687229/34.
Повний текст джерелаGurung, Topraj. "Compact connectivity representation for triangle meshes." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47709.
Повний текст джерелаLee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Повний текст джерелаBandyopadhyay, Bortik. "Querying Structured Data via Informative Representations." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595447189545086.
Повний текст джерелаGkirtzou, Aikaterini. "Sparsity regularization and graph-based representation in medical imaging." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00960163.
Повний текст джерелаPeng, Chong. "Integrating Feature and Graph Learning with Factorization Models for Low-Rank Data Representation." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1464.
Повний текст джерелаKim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.
Повний текст джерелаCommittee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Soares, Telma Woerle de Lima. "Estruturas de dados eficientes para algoritmos evolutivos aplicados a projeto de redes." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28052009-163303/.
Повний текст джерелаNetwork design problems (NDPs) are very important since they involve several applications from areas of Engineering and Sciences. In order to solve the limitations of traditional algorithms for NDPs that involve real world complex networks (in general, modeled by large-scale complete or sparse graphs), heuristics, such as evolutionary algorithms (EAs), have been investigated. Recent researches have shown that appropriate data structures can improve EA performance when applied to NDPs. One of these data structures is the Node-depth Encoding (NDE). In general, the performance of EAs with NDE has presented relevant results for large-scale NDPs. This thesis investigates the development of a new representation, based on NDE, called Node-depth-degree Encoding (NDDE). The NDDE is composed for improvements of the NDE operators and the development of new reproduction operators that enable the recombination of solutions. In this way, we developed a recombination operator to work with both non-complete and complete graphs, called EHR (Evolutionary History Recombination Operator). We also developed two other operators to work only with complete graphs, named NOX and NPBX. These improvements have the advantage of retaining the computational complexity of the operators relatively low in order to improve the EA performance. The analysis of representation properties have shown that NDDE is a redundant representation and, for this reason, we proposed some strategies to avoid it. This analysis also showed that EHR has low running time and it does not have bias, moreover, it revealed that NOX and NPBX have bias to trees like stars. The application of an EA using the NDDE to classic NDPs, such as, optimal communication spanning tree, degree-constraint minimum spanning tree and one-max tree, showed that the larger the instance is, the better the performance will be in comparison whit other EAs applied to NDPs in the literatura. An EA using the NDE with EHR was applied to a real-world NDP of reconfiguration of energy distribution systems. The results showed that EHR significantly decrease the convergence time of the EA
Книги з теми "Structural Graph Representations"
Marie-Laure, Mugnier, ed. Graph-based knowledge representation: Computational foundations of conceptual graphs. New York: Springer, 2009.
Знайти повний текст джерелаMarie-Laure, Mugnier, ed. Graph-based knowledge representation: Computational foundations of conceptual graphs. New York: Springer, 2009.
Знайти повний текст джерелаCochez, Michael, Madalina Croitoru, Pierre Marquis, and Sebastian Rudolph, eds. Graph Structures for Knowledge Representation and Reasoning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8.
Повний текст джерелаCroitoru, Madalina, Pierre Marquis, Sebastian Rudolph, and Gem Stapleton, eds. Graph Structures for Knowledge Representation and Reasoning. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28702-7.
Повний текст джерелаCroitoru, Madalina, Sebastian Rudolph, Nic Wilson, John Howse, and Olivier Corby, eds. Graph Structures for Knowledge Representation and Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29449-5.
Повний текст джерелаCroitoru, Madalina, Sebastian Rudolph, Stefan Woltran, and Christophe Gonzales, eds. Graph Structures for Knowledge Representation and Reasoning. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04534-4.
Повний текст джерелаCroitoru, Madalina, Pierre Marquis, Sebastian Rudolph, and Gem Stapleton, eds. Graph Structures for Knowledge Representation and Reasoning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78102-0.
Повний текст джерелаM, Tepfenhart William, Dick Judith P, and Sowa John F, eds. Conceptual structures, current practices: Second International Conference on Conceptual Structures, ICCS'94, College Park, Maryland, USA, August 16-20, 1994 : proceedings. Berlin: Springer-Verlag, 1994.
Знайти повний текст джерелаInternational Conference on Conceptual Structures (4th 1996 Sydney, N.S.W.). Conceptual structures: Knowledge representation as interlingua : 4th International Conference on Conceptual Structures, ICCS '96, Sydney, Australia, August 19-22, 1996 : proceedings. Berlin: Springer, 1996.
Знайти повний текст джерелаDickson, Lukose, ed. Conceptual structures: Fulfilling Peirce's dream : fifth International Conference on Conceptual Structures, ICCS'97, Seattle, Washington, USA, August 3-8, 1997 : proceedings. Berlin: Springer, 1997.
Знайти повний текст джерелаЧастини книг з теми "Structural Graph Representations"
Erus, Güray, and Nicolas Loménie. "Automatic Learning of Structural Models of Cartographic Objects." In Graph-Based Representations in Pattern Recognition, 273–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31988-7_26.
Повний текст джерелаFischer, Andreas, Seiichi Uchida, Volkmar Frinken, Kaspar Riesen, and Horst Bunke. "Improving Hausdorff Edit Distance Using Structural Node Context." In Graph-Based Representations in Pattern Recognition, 148–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_15.
Повний текст джерелаSanromà, Gerard, René Alquézar, and Francesc Serratosa. "Smooth Simultaneous Structural Graph Matching and Point-Set Registration." In Graph-Based Representations in Pattern Recognition, 142–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20844-7_15.
Повний текст джерелаEisenstat, Stanley C., and Joseph W. H. Liu. "Structural Representations of Schur Complements in Sparse Matrices." In Graph Theory and Sparse Matrix Computation, 85–100. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8369-7_4.
Повний текст джерелаArrivault, Denis, Noël Richard, Christine Fernandez-Maloigne, and Philippe Bouyer. "Collaboration Between Statistical and Structural Approaches for Old Handwritten Characters Recognition." In Graph-Based Representations in Pattern Recognition, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31988-7_28.
Повний текст джерелаSolé-Ribalta, Albert, and Francesc Serratosa. "A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs." In Graph-Based Representations in Pattern Recognition, 164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02124-4_17.
Повний текст джерелаJiang, X. Y., and H. Bunke. "Including geometry in graph representations: A quadratic-time graph isomorphism algorithm and its applications." In Advances in Structural and Syntactical Pattern Recognition, 110–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61577-6_12.
Повний текст джерелаWashietl, Stefan, and Tanja GesellGesell. "Graph Representations and Algorithms in Computational Biology of RNA Secondary Structure." In Structural Analysis of Complex Networks, 421–37. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4789-6_17.
Повний текст джерелаSanders, Peter, Kurt Mehlhorn, Martin Dietzfelbinger, and Roman Dementiev. "Graph Representation." In Sequential and Parallel Algorithms and Data Structures, 259–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25209-0_8.
Повний текст джерелаWare, Colin. "The Visual Representation of Information Structures." In Graph Drawing, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44541-2_1.
Повний текст джерелаТези доповідей конференцій з теми "Structural Graph Representations"
Xu, Jiacheng, Xipeng Qiu, Kan Chen, and Xuanjing Huang. "Knowledge Graph Representation with Jointly Structural and Textual Encoding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/183.
Повний текст джерелаBorutta, Felix, Julian Busch, Evgeniy Faerman, Adina Klink, and Matthias Schubert. "Structural Graph Representations based on Multiscale Local Network Topologies." In WI '19: IEEE/WIC/ACM International Conference on Web Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3350546.3352505.
Повний текст джерелаDasoulas, George, Ludovic Dos Santos, Kevin Scaman, and Aladin Virmaux. "Coloring Graph Neural Networks for Node Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/294.
Повний текст джерелаRao, Haocong, Shihao Xu, Xiping Hu, Jun Cheng, and Bin Hu. "Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/135.
Повний текст джерелаLee, See Hian, Feng Ji, and Wee Peng Tay. "SGAT: Simplicial Graph Attention Network." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/443.
Повний текст джерелаLu, Yuyin, Xin Cheng, Ziran Liang, and Yanghui Rao. "Graph-based Dynamic Word Embeddings." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/594.
Повний текст джерелаHahn, Elad, and Offer Shai. "A Single Universal Construction Rule for the Structural Synthesis of Mechanisms." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59133.
Повний текст джерелаHu, Binbin, Zhengwei Wu, Jun Zhou, Ziqi Liu, Zhigang Huangfu, Zhiqiang Zhang, and Chaochao Chen. "MERIT: Learning Multi-level Representations on Temporal Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/288.
Повний текст джерелаJu, Wei, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang. "TGNN: A Joint Semi-supervised Framework for Graph-level Classification." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/295.
Повний текст джерелаLyu, Gengyu, Yanan Wu, and Songhe Feng. "Deep Graph Matching for Partial Label Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/459.
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