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Graph theoretic approaches for analy...

Meghanathan, Natarajan, (1977-)

Graph theoretic approaches for analyzing large-scale social networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Graph theoretic approaches for analyzing large-scale social networksNatarajan Meghanathan, editor.
其他作者:	Meghanathan, Natarajan,
出版者:	Hershey, Pennsylvania :IGI Global,[2018]
面頁冊數:	1 online resource (xxi, 355 p.)
標題:	Social networks.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-2814-2
ISBN:	9781522528159 (ebook)

Graph theoretic approaches for analyzing large-scale social networks
Graph theoretic approaches for analyzing large-scale social networks[electronic resource] /Natarajan Meghanathan, editor. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxi, 355 p.)

Includes bibliographical references and index.

Chapter 1. Walk through social network analysis: opportunities, limitations, and threats -- Chapter 2. Graph tools for social network analysis -- Chapter 3. An approach to mining information from telephone graph using graph mining techniques -- Chapter 4. A dynamic and context-aware social network approach for multiple criteria decision making through a graph-based knowledge learning -- Chapter 5. Undirected bipartite networks as an alternative methodology to probabilistic exploration: online interaction and academic attainment in MOOC -- Chapter 6. Social network analysis of different parameters derived from real-time Facebook profiles -- Chapter 7. Context specific modeling of communicational and informational content in Facebook -- Chapter 8. Hadoop-based distributed K-Shell decomposition for social networks -- Chapter 9. Parallelizing large-scale graph algorithms using the Apache Spark-Distributed memory system -- Chapter 10. Link prediction in social networks -- Chapter 11. Visualizing co-authorship social networks and collaboration recommendations with CNARe -- Chapter 12. Community detection in large-scale social networks: a survey -- Chapter 13. Spreading activation connectivity based approach to network clustering -- Chapter 14. Scalable method for information spread control in social networks -- Chapter 15. The eternal-return model of human mobility and its impact on information flow -- Chapter 16. Towards a Unified Semantic Model for online social networks to ensure interoperability and aggregation for analysis -- Chapter 17. Can we trust the health information we find online? Identification of influential nodes.

Restricted to subscribers or individual electronic text purchasers.

"This book brings together the recent research advances in the field of graph theory for analyzing large-scale social networks. It brings together the research advances in state-of-the-art graph theory algorithms and techniques that have contributed to the effective analysis of social networks, especially those networks that generate significant amount of data and involve several thousands of users"--

ISBN: 9781522528159 (ebook)Subjects--Topical Terms:

207332
Social networks.

LC Class. No.: HM741 / .G73 2018e

Dewey Class. No.: 302.3

Graph theoretic approaches for analyzing large-scale social networks
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504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Walk through social network analysis: opportunities, limitations, and threats -- Chapter 2. Graph tools for social network analysis -- Chapter 3. An approach to mining information from telephone graph using graph mining techniques -- Chapter 4. A dynamic and context-aware social network approach for multiple criteria decision making through a graph-based knowledge learning -- Chapter 5. Undirected bipartite networks as an alternative methodology to probabilistic exploration: online interaction and academic attainment in MOOC -- Chapter 6. Social network analysis of different parameters derived from real-time Facebook profiles -- Chapter 7. Context specific modeling of communicational and informational content in Facebook -- Chapter 8. Hadoop-based distributed K-Shell decomposition for social networks -- Chapter 9. Parallelizing large-scale graph algorithms using the Apache Spark-Distributed memory system -- Chapter 10. Link prediction in social networks -- Chapter 11. Visualizing co-authorship social networks and collaboration recommendations with CNARe -- Chapter 12. Community detection in large-scale social networks: a survey -- Chapter 13. Spreading activation connectivity based approach to network clustering -- Chapter 14. Scalable method for information spread control in social networks -- Chapter 15. The eternal-return model of human mobility and its impact on information flow -- Chapter 16. Towards a Unified Semantic Model for online social networks to ensure interoperability and aggregation for analysis -- Chapter 17. Can we trust the health information we find online? Identification of influential nodes.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book brings together the recent research advances in the field of graph theory for analyzing large-scale social networks. It brings together the research advances in state-of-the-art graph theory algorithms and techniques that have contributed to the effective analysis of social networks, especially those networks that generate significant amount of data and involve several thousands of users"-- $c Provided by publisher.
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