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Books on the topic 'Graph embeddings'

Author: Grafiati
Published: 28 June 2021
Last updated: 14 September 2024

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1

L, Miller Gary, and Langley Research Center, eds. Graph embeddings and Laplacian eigenvalues. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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2

L, Miller Gary, and Langley Research Center, eds. Graph embeddings and Laplacian eigenvalues. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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3

Yanpei, Liu. Embeddability in graphs. Beijing, China: Science Press, 1995.

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4

Institute for Computer Applications in Science and Engineering., ed. Graph embeddings, symmetric real matrices, and generalized inverses. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.

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5

Institute for Computer Applications in Science and Engineering., ed. Graph embeddings, symmetric real matrices, and generalized inverses. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.

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6

Alfaqeeh, Mosab, and David B. Skillicorn. Finding Communities in Social Networks Using Graph Embeddings. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60916-9.

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7

Guattery, Stephen. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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8

Center, Langley Research, ed. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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9

Deza. Geometry of cuts and metrics. New York: Springer-Verlag, 1997.

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10

Sun, Timothy. Testing Convexity and Acyclicity, and New Constructions for Dense Graph Embeddings. [New York, N.Y.?]: [publisher not identified], 2019.

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11

Fu, Yun, and Yunqian Ma, eds. Graph Embedding for Pattern Analysis. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4457-2.

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12

Shaw, Blake. Graph Embedding and Nonlinear Dimensionality Reduction. [New York, N.Y.?]: [publisher not identified], 2011.

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13

Paulheim, Heiko, Petar Ristoski, and Jan Portisch. Embedding Knowledge Graphs with RDF2vec. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30387-6.

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14

Cai, Jiazhen. Counting embeddings of planar graphs using DFS trees. New York: Courant Institute of Mathematical Sciences, New York University, 1992.

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15

Riesen, Kaspar. Graph classification and clustering based on vector space embedding. New Jersey: World Scientific, 2010.

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16

Alfaqeeh, Mosab. Finding Communities in Social Networks Using Graph Embeddings. Springer, 2024.

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17

Vassiliev, V. A., Sergei K. Lando, and R. V. Gamkrelidze. Graphs on Surfaces and Their Applications. Springer, 2012.

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18

Gamkrelidze, R. V., Sergei K. Lando, Alexander K. Zvonkin, and V. A. Vassiliev. Graphs on Surfaces and Their Applications. Springer London, Limited, 2006.

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19

Lando, Sergei K. Graphs on Surfaces and Their Applications. Springer, 2010.

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20

Lando, Sergei K., Alexander K. Zvonkin, and D. B. Zagier. Graphs on Surfaces and Their Applications. Springer, 2004.

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21

Embedding Planar Graphs. United States: University of Illinois, 2016. http://dx.doi.org/10.4135/9781529773132.

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22

Graph Embedding For Pattern Analysis. Springer-Verlag New York Inc., 2012.

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23

Ma, Yunqian, and Yun Fu. Graph Embedding for Pattern Analysis. Springer, 2012.

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24

Ma, Yunqian, and Yun Fu. Graph Embedding for Pattern Analysis. Springer New York, 2014.

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25

Ma, Yunqian, and Yun Fu. Graph Embedding for Pattern Analysis. Springer London, Limited, 2012.

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26

Geometry of Cuts and Metrics Algorithms and Combinatorics. Springer, 2009.

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27

Ristoski, Petar, and Jan Portisch. Embedding Knowledge Graphs with RDF2vec. Springer International Publishing AG, 2023.

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28

Qingfeng, Chen, Wei Lan, Yi-Ping Phoebe Chen, and Wilson Wen Bin Goh, eds. Graph Embedding Methods for Multiple-Omics Data Analysis. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-600-5.

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29

Graph Classification and Clustering Based on Vector Space Embedding. World Scientific Publishing Co Pte Ltd, 2010.

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30

Graph Classification and Clustering Based on Vector Space Embedding. World Scientific Publishing Co Pte Ltd, 2010.

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31

Geometry of Semilinear Embeddings: Relations to Graphs and Codes. World Scientific Publishing Co Pte Ltd, 2015.

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32

Geometry of Semilinear Embeddings: Relations to Graphs and Codes. World Scientific Publishing Co Pte Ltd, 2015.

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33

Geometry of Semilinear Embeddings: Relations to Graphs and Codes. World Scientific Publishing Co Pte Ltd, 2015.

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34

Aggarwal, Manasvi, and M. N. Murty. Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs. Springer Singapore Pte. Limited, 2020.

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35

Dodd, Emlyn, and Dimitri Van Limbergen, eds. Methods in Ancient Wine Archaeology. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350346680.

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Abstract:
Bringing together a wide array of modern scientific techniques and interdisciplinary approaches, this book provides an accessible guide to the methods that form the current bedrock of research into Roman, and more broadly ancient, wine. Chapters are arranged into thematic sections, covering biomolecular archaeology and chemical analysis, archaeobotany and palynology, vineyard and landscape archaeology and computational and experimental archaeology. These include discussions of some of the most recent techniques, such as ancient DNA and organic residue analyses, geophysical prospection, multispectral imaging and spatial and climatic modelling. While most of the content is of direct relevance to the Roman Mediterranean, the assortment of detailed case studies, methodological outlines and broader ‘state of the field’ reflections is of equal use to researchers working across disparate disciplines, geographies, and chronologies. The study of ancient Roman wine has been dominated until recently by traditional archaeological analyses focused upon production facilities and ceramic evidence related to transport. While such architecture and artefact-focussed approaches provide a fundamental foundation for our understanding of this topic, they fail to provide the requisite nuance to answer other questions regarding grape cultivation and wine production, consumption, use and trade. As the first compendium of its kind, this book supports the embedding of modern scientific and experimental techniques into archaeological fieldwork, research and laboratory analysis, pushing the boundaries of what questions can be explored, and serving as a launching point for future avenues of interdisciplinary research. This volume presents an array of cutting-edge scientific and archaeological methodologies used in the study of vine-growing and winemaking in the Graeco-Roman Mediterranean. Recent work on this subject tends to be dominated by traditional archaeological analyses of press facilities and, above all, ceramic evidence. These approaches lack the nuance to answer several vital questions about Greek and Roman grapevine cultivation and wine production, consumption, use and trade. In response, modern scientific techniques are increasingly filling these gaps in both fieldwork and laboratory contexts. By bringing together a wide range of disciplinary approaches in one location, this book provides an accessible guide to the scientific methodologies that form the new bedrock of current research on ancient wine. Arranged into thematic sections covering biomolecular archaeology and chemical analysis, archaeobotany and palynology, vineyard and landscape archaeology, and computational modelling and experimental archaeology, its detailed exemplary case studies and ‘state of the field’ chapters can be immediately utilized by researchers working across disparate fields, chronologies and research contexts as reliable points of reference. As the first compendium of its kind, it aims to foster interconnection of modern scientific technologies with archaeological fieldwork and analysis, pushing the boundaries of what questions can be explored and serving as a foundation for future avenues of research into ancient wine production.
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