Social networks with rich edge semantics / Quan Zheng, David Skillicorn.
"Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and n...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press, Taylor & Francis Group,
[2017]
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| Edition: | First edition. |
| Series: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Subjects: |
MARC
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| 100 | 1 | |a Zheng, Quan |c (Telecommunications engineer), |e author. |0 http://id.loc.gov/authorities/names/n2017059548 |1 https://id.oclc.org/worldcat/entity/E39PCjFkGd3qYtgqWGq7TtrGtq | |
| 245 | 1 | 0 | |a Social networks with rich edge semantics / |c Quan Zheng, David Skillicorn. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, Taylor & Francis Group, |c [2017] | |
| 300 | |a 1 online resource (xx, 210 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC data mining and knowledge discovery series | |
| 520 | 2 | |a "Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriates hows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks."--Provided by publisher | |
| 505 | 0 | |a Cover ; Half title ; Published titles ; Title ; Copyright ; Contents ; Preface ; List of figures ; List of tables ; Glossary ; Chapter 1 introduction; 1.1 what is a social network. | |
| 505 | 8 | |a 1.2 multiple aspects of relationships 1.3 formally representing social networks ; Chapter 2 the core model. | |
| 505 | 8 | |a 2.1 representing networks to understand their structures 2.2 building layered models ; 2.3 summary ; Chapter 3 background; 3.1 graph theory background. | |
| 505 | 8 | |a 3.2 spectral graph theory 3.2.1 the unnormalized graph laplacian ; 3.2.2 the normalized graph laplacians ; 3.3 spectral pipeline. | |
| 505 | 8 | |a 3.4 spectral approaches to clustering 3.4.1 undirected spectral clustering algorithms ; 3.4.2 which laplacian clustering should be used. | |
| 542 | 1 | |f This work is licensed under a Creative Commons license |u https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode | |
| 504 | |a Includes bibliographical references and index. | ||
| 650 | 0 | |a Social networks |x Mathematical models. | |
| 650 | 0 | |a Semantic Web. |0 http://id.loc.gov/authorities/subjects/sh2002000569 | |
| 650 | 0 | |a Social media. |0 http://id.loc.gov/authorities/subjects/sh2006007023 | |
| 650 | 7 | |a Social media. |2 fast | |
| 650 | 7 | |a Semantic Web. |2 fast | |
| 650 | 7 | |a Social networks |x Mathematical models. |2 fast | |
| 700 | 1 | |a Skillicorn, David B. |0 http://id.loc.gov/authorities/names/n86036061 |1 http://isni.org/isni/0000000116234897 | |
| 758 | |i has work: |a Social networks with rich edge semantics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGCxPkkjhw8W86hjVC7Brq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |z 9781315390628 |z 9781315390611 |
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| 830 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series. |0 http://id.loc.gov/authorities/names/no2007060486 | |
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