Littérature scientifique sur le sujet « Learning on graphs »
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Articles de revues sur le sujet "Learning on graphs"
Huang, Xueqin, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang et Ailin Liang. « Parallel Learning of Dynamics in Complex Systems ». Systems 10, no 6 (15 décembre 2022) : 259. http://dx.doi.org/10.3390/systems10060259.
Texte intégralZeng, Jiaqi, et Pengtao Xie. « Contrastive Self-supervised Learning for Graph Classification ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 12 (18 mai 2021) : 10824–32. http://dx.doi.org/10.1609/aaai.v35i12.17293.
Texte intégralFionda, Valeria, et Giuseppe Pirrò. « Learning Triple Embeddings from Knowledge Graphs ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 04 (3 avril 2020) : 3874–81. http://dx.doi.org/10.1609/aaai.v34i04.5800.
Texte intégralZainullina, R. « Automatic Graph Generation for E-learning Systems ». Bulletin of Science and Practice 7, no 6 (15 juin 2021) : 12–16. http://dx.doi.org/10.33619/2414-2948/67/01.
Texte intégralHu, Shengze, Weixin Zeng, Pengfei Zhang et Jiuyang Tang. « Neural Graph Similarity Computation with Contrastive Learning ». Applied Sciences 12, no 15 (29 juillet 2022) : 7668. http://dx.doi.org/10.3390/app12157668.
Texte intégralXiang, Xintao, Tiancheng Huang et Donglin Wang. « Learning to Evolve on Dynamic Graphs (Student Abstract) ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 11 (28 juin 2022) : 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.
Texte intégralKim, Jaehyeon, Sejong Lee, Yushin Kim, Seyoung Ahn et Sunghyun Cho. « Graph Learning-Based Blockchain Phishing Account Detection with a Heterogeneous Transaction Graph ». Sensors 23, no 1 (1 janvier 2023) : 463. http://dx.doi.org/10.3390/s23010463.
Texte intégralMa, Yunpu, et Volker Tresp. « Quantum Machine Learning Algorithm for Knowledge Graphs ». ACM Transactions on Quantum Computing 2, no 3 (30 septembre 2021) : 1–28. http://dx.doi.org/10.1145/3467982.
Texte intégralMa, Guixiang, Nesreen K. Ahmed, Theodore L. Willke et Philip S. Yu. « Deep graph similarity learning : a survey ». Data Mining and Knowledge Discovery 35, no 3 (24 mars 2021) : 688–725. http://dx.doi.org/10.1007/s10618-020-00733-5.
Texte intégralLee, Namkyeong, Junseok Lee et Chanyoung Park. « Augmentation-Free Self-Supervised Learning on Graphs ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 7 (28 juin 2022) : 7372–80. http://dx.doi.org/10.1609/aaai.v36i7.20700.
Texte intégralThèses sur le sujet "Learning on graphs"
Vitale, F. « FAST LEARNING ON GRAPHS ». Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155500.
Texte intégralIrniger, Christophe-André. « Graph matching filtering databases of graphs using machine learning techniques ». Berlin Aka, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2677754&prov=M&dok_var=1&dok_ext=htm.
Texte intégralSimonovsky, Martin. « Deep learning on attributed graphs ». Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1133/document.
Texte intégralGraph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines, describing chemical compounds, surfaces of three-dimensional models, social interactions, or knowledge bases, to name only a few. There is a natural desire for understanding such data better. Deep learning (DL) has achieved significant breakthroughs in a variety of machine learning tasks in recent years, especially where data is structured on a grid, such as in text, speech, or image understanding. However, surprisingly little has been done to explore the applicability of DL on graph-structured data directly.The goal of this thesis is to investigate architectures for DL on graphs and study how to transfer, adapt or generalize concepts working well on sequential and image data to this domain. We concentrate on two important primitives: embedding graphs or their nodes into a continuous vector space representation (encoding) and, conversely, generating graphs from such vectors back (decoding). To that end, we make the following contributions.First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like operation on graphs performed in the spatial domain where filters are dynamically generated based on edge attributes. The method is used to encode graphs with arbitrary and varying structure.Second, we propose SuperPoint Graph, an intermediate point cloud representation with rich edge attributes encoding the contextual relationship between object parts. Based on this representation, ECC is employed to segment large-scale point clouds without major sacrifice in fine details.Third, we present GraphVAE, a graph generator allowing to decode graphs with variable but upper-bounded number of nodes making use of approximate graph matching for aligning the predictions of an autoencoder with its inputs. The method is applied to the task of molecule generation
Rosar, Kós Lassance Carlos Eduardo. « Graphs for deep learning representations ». Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.
Texte intégralIn recent years, Deep Learning methods have achieved state of the art performance in a vast range of machine learning tasks, including image classification and multilingual automatic text translation. These architectures are trained to solve machine learning tasks in an end-to-end fashion. In order to reach top-tier performance, these architectures often require a very large number of trainable parameters. There are multiple undesirable consequences, and in order to tackle these issues, it is desired to be able to open the black boxes of deep learning architectures. Problematically, doing so is difficult due to the high dimensionality of representations and the stochasticity of the training process. In this thesis, we investigate these architectures by introducing a graph formalism based on the recent advances in Graph Signal Processing (GSP). Namely, we use graphs to represent the latent spaces of deep neural networks. We showcase that this graph formalism allows us to answer various questions including: ensuring generalization abilities, reducing the amount of arbitrary choices in the design of the learning process, improving robustness to small perturbations added to the inputs, and reducing computational complexity
Ghiasnezhad, Omran Pouya. « Rule Learning in Knowledge Graphs ». Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382680.
Texte intégralThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Fan, Shuangfei. « Deep Representation Learning on Labeled Graphs ». Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96596.
Texte intégralDoctor of Philosophy
Graphs are one of the most important and powerful data structures for conveying the complex and correlated information among data points. In this research, we aim to provide more robust and accurate models for some graph specific tasks, such as collective classification and graph generation, by designing deep learning models to learn better task-specific representations for graphs. First, we studied the collective classification problem in graphs and proposed recurrent collective classification, a variant of the iterative classification algorithm that is more robust to situations where predictions are noisy or inaccurate. Then we studied the problem of graph generation using deep generative models. We first proposed a deep generative model using the GAN framework that generates labeled graphs. Then in order to support more applications and also get more control over the generated graphs, we extended the problem of graph generation to conditional graph generation which can then be applied to various applications for modeling graph evolution and transformation.
Rommedahl, David, et Martin Lindström. « Learning Sparse Graphs for Data Prediction ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295623.
Texte intégralGrafstrukturer kan ofta användas för att beskriva komplex data. I många tillämpningar är grafstrukturen inte känd, utan måste läras från data. Vidare beskrivs verklig data ofta naturligt av glesa grafer. I detta projekt har vi försökt återskapa resultaten från ett tidigare forskningsarbete, nämligen att lära en graf som kan användas för prediktion med en ℓ1pennaliserad LASSO-metod. Vi föreslår även andra metoder för inlärning och utvärdering av grafen. Vi har testat metoderna på syntetisk data och verklig temperaturdata från Sverige. Resultaten visar att vi inte kan återskapa de tidigare forskarnas resultat, men vi lyckas lära in glesa grafer som kan användas för prediktion. Ytterligare arbete krävs för att verifiera våra resultat.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Xu, Keyulu. « Graph structures, random walks, and all that : learning graphs with jumping knowledge networks ». Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121660.
Texte intégralThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-54).
Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical properties of drugs, community detection in social networks, and modeling interactions in physical systems. Recent deep learning approaches for graph representation learning, namely Graph Neural Networks (GNNs), follow a neighborhood aggregation procedure, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. We analyze some important properties of these models, and propose a strategy to overcome the limitations. In particular, the range of neighboring nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture - jumping knowledge (JK) networks that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
by Keyulu Xu.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Freeman, Guy. « Learning and predicting with chain event graphs ». Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/4529/.
Texte intégralPasteris, S. U. « Efficient algorithms for online learning over graphs ». Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
Texte intégralLivres sur le sujet "Learning on graphs"
Irniger, Christophe-André Mario. Graph matching : Filtering databases of graphs using machine learning techniques. Berlin : AKA, 2005.
Trouver le texte intégralLet's eat lunch : Learning about picture graphs. New York : Rosen Classroom Books & Materials, 2004.
Trouver le texte intégralSwan, Malcolm. Learning the language of functions and graphs. [Nottingham] : [Shell Centre for Mathematical Education, University of Nottingham], 1988.
Trouver le texte intégralOn the trail with Lewis and Clark : Learning to use line graphs. New York : Rosen Pub. Group, 2004.
Trouver le texte intégralFung, P. W. Designing an intelligent case-based learning environment in physics with conceptual graphs. Manchester : UMIST, 1996.
Trouver le texte intégralBayesian networks and decision graphs. New York : Springer, 2001.
Trouver le texte intégralSudre, Carole H., Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput et al., dir. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6.
Texte intégralHamilton, William L. Graph Representation Learning. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01588-5.
Texte intégralSubramanya, Amarnag, et Partha Pratim Talukdar. Graph-Based Semi-Supervised Learning. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01571-7.
Texte intégralZhang, Daoqiang, Luping Zhou, Biao Jie et Mingxia Liu, dir. Graph Learning in Medical Imaging. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35817-4.
Texte intégralChapitres de livres sur le sujet "Learning on graphs"
Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid et al. « Graphs ». Dans Encyclopedia of Machine Learning, 479–82. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_352.
Texte intégralWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. « Directed Graphs ». Dans Encyclopedia of Machine Learning, 279. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_218.
Texte intégralCesa-Bianchi, Nicolò, Claudio Gentile et Fabio Vitale. « Learning Unknown Graphs ». Dans Lecture Notes in Computer Science, 110–25. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04414-4_13.
Texte intégralJensen, Tommy R. « Graphs ». Dans Encyclopedia of Machine Learning and Data Mining, 592–96. Boston, MA : Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_352.
Texte intégralAggarwal, Manasvi, et M. N. Murty. « Embedding Graphs ». Dans Machine Learning in Social Networks, 89–104. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4022-0_5.
Texte intégralWebb, Cerian Ruth, et Mirela Domijan. « Graphs and Plots ». Dans Learning Materials in Biosciences, 85–104. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21337-4_7.
Texte intégralMichelucci, Umberto. « Computational Graphs and TensorFlow ». Dans Applied Deep Learning, 1–29. Berkeley, CA : Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8_1.
Texte intégralLiang, De-Ming, et Yu-Feng Li. « Learning Safe Graph Construction from Multiple Graphs ». Dans Communications in Computer and Information Science, 41–54. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2122-1_4.
Texte intégralBaxter, Nancy, Ed Dubinsky et Gary Levin. « Relations and Graphs ». Dans Learning Discrete Mathematics with ISETL, 363–404. New York, NY : Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3592-7_8.
Texte intégralJappy, Pascal, et Richard Nock. « PAC learning conceptual graphs ». Dans Conceptual Structures : Theory, Tools and Applications, 303–15. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054923.
Texte intégralActes de conférences sur le sujet "Learning on graphs"
Zhang, Ziwei, Xin Wang et Wenwu Zhu. « Automated Machine Learning on Graphs : A Survey ». Dans 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.2021/637.
Texte intégralKim, Seoyoon, Seongjun Yun et Jaewoo Kang. « DyGRAIN : An Incremental Learning Framework for Dynamic Graphs ». Dans 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.2022/438.
Texte intégralDas, Bishwadeep, et Elvin Isufi. « Graph Filtering Over Expanding Graphs ». Dans 2022 IEEE Data Science and Learning Workshop (DSLW). IEEE, 2022. http://dx.doi.org/10.1109/dslw53931.2022.9820066.
Texte intégralBarros, Claudio D. T., Daniel N. R. da Silva et Fabio A. M. Porto. « Machine Learning on Graph-Structured Data ». Dans Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18179.
Texte intégralZhang, Chuxu, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla et Huan Liu. « Few-Shot Learning on Graphs ». Dans 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.2022/789.
Texte intégralSokolova, A. A., et A. Yu Syshchikov. « THE APPLICATION OF MACHINE LEARNING METHODS TO PROBLEMS ON GRAPHS ». Dans Aerospace instrumentation and operational technologies. Saint Petersburg State University of Aerospace Instrumentation, 2021. http://dx.doi.org/10.31799/978-5-8088-1554-4-2021-2-321-324.
Texte intégralYou, Jiaxuan, Tianyu Du et Jure Leskovec. « ROLAND : Graph Learning Framework for Dynamic Graphs ». Dans KDD '22 : The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3534678.3539300.
Texte intégralYang, Peng, Peilin Zhao et Xin Gao. « Bandit Online Learning on Graphs via Adaptive Optimization ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.
Texte intégralIslam, Md Kamrul, Sabeur Aridhi et Malika Smail-Tabbone. « Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs ». Dans 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111106.
Texte intégralLyford, Alex, et Lonneke Boels. « Using Machine Learning to Understand Students’ Gaze Patterns on Graphing Tasks ». Dans Bridging the Gap : Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t8d2.
Texte intégralRapports d'organisations sur le sujet "Learning on graphs"
Kriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.
Texte intégralGoldberg, Sean, et Daisy Zhe Wang. Graph Learning in Knowledge Bases. Office of Scientific and Technical Information (OSTI), septembre 2017. http://dx.doi.org/10.2172/1390764.
Texte intégralHolder, Lawrence B., et Diane J. Cook. Graph-Based Structural Pattern Learning. Fort Belvoir, VA : Defense Technical Information Center, juillet 2006. http://dx.doi.org/10.21236/ada456904.
Texte intégralBabkin, Vladyslav V., Viktor V. Sharavara, Volodymyr V. Sharavara, Vladyslav V. Bilous, Andrei V. Voznyak et Serhiy Ya Kharchenko. Using augmented reality in university education for future IT specialists : educational process and student research work. CEUR Workshop Proceedings, juillet 2021. http://dx.doi.org/10.31812/123456789/4632.
Texte intégralQi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin et Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, décembre 2020. http://dx.doi.org/10.37686/ser.v1i2.77.
Texte intégralMichalenko, Joshua, Indu Manickam et Stephen Heck. GraphAlign : Graph-Enabled Machine Learning for Seismic Event Filtering. Office of Scientific and Technical Information (OSTI), septembre 2022. http://dx.doi.org/10.2172/1887336.
Texte intégralBilous, Vladyslav V., Volodymyr V. Proshkin et Oksana S. Lytvyn. Development of AR-applications as a promising area of research for students. [б. в.], novembre 2020. http://dx.doi.org/10.31812/123456789/4409.
Texte intégralHomel, M. A., C. S. Sherman et J. P. Morris. Machine Learning for Constitutive Modeling on a Graphics Processing Unit. Office of Scientific and Technical Information (OSTI), novembre 2019. http://dx.doi.org/10.2172/1576907.
Texte intégralGustafsson, Martin, et Nick Taylor. The Politics of Improving Learning Outcomes in South Africa. Research on Improving Systems of Education (RISE), octobre 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe03.
Texte intégralLi, Wenting. Robust Fault Location in Power Grids through Graph Learning at Low Label Rates. Office of Scientific and Technical Information (OSTI), février 2021. http://dx.doi.org/10.2172/1768426.
Texte intégral