Graph theoretic approaches for analyzing large-scale social networks:
"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,...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
[c2018]
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | "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"-- |
| Physical Description: | 23 PDFs (xxi, 355 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781522528159 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
Staff View
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| 245 | 0 | 0 | |a Graph theoretic approaches for analyzing large-scale social networks |c Natarajan Meghanathan, editor. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c [c2018] | |
| 300 | |a 23 PDFs (xxi, 355 Seiten) | ||
| 336 | |a text |2 rdacontent | ||
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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. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | 0 | |a Description based on title screen (IGI Global, viewed 07/18/2017). | |
| 650 | 0 | |a Graph theory. | |
| 650 | 0 | |a Social networks. | |
| 650 | 0 | |a Social sciences |x Network analysis. | |
| 650 | 0 | |a Sociometry. | |
| 653 | |a Content Specific Modeling. | ||
| 653 | |a Distributed Memory. | ||
| 653 | |a Graph Mining. | ||
| 653 | |a Influence Maximization. | ||
| 653 | |a Information Spread Control. | ||
| 653 | |a Link Prediction. | ||
| 653 | |a Probabilistic Exploration. | ||
| 700 | 1 | |a Meghanathan, Natarajan |d 1977- |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
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| contents | 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. |
| ctrlnum | (CaBNVSL)slc19685926 (OCoLC)988656959 |
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| indexdate | 2025-03-18T14:30:28Z |
| institution | BVB |
| isbn | 9781522528159 |
| language | English |
| oclc_num | 988656959 |
| open_access_boolean | |
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| owner_facet | DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 23 PDFs (xxi, 355 Seiten) Also available in print. |
| psigel | ZDB-98-IGB FWS_PDA_IGB ZDB-98-IGB |
| publishDate | 2018 |
| publishDateSearch | 2018 |
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| publisher | IGI Global |
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| spelling | Graph theoretic approaches for analyzing large-scale social networks Natarajan Meghanathan, editor. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global [c2018] 23 PDFs (xxi, 355 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier 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"-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 07/18/2017). Graph theory. Social networks. Social sciences Network analysis. Sociometry. Content Specific Modeling. Distributed Memory. Graph Mining. Influence Maximization. Information Spread Control. Link Prediction. Probabilistic Exploration. Meghanathan, Natarajan 1977- editor. IGI Global, publisher. (Original) (DLC)2017010785 Print version: 1522528148 9781522528142 (DLC) 2017010785 |
| spellingShingle | Graph theoretic approaches for analyzing large-scale social networks 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. Graph theory. Social networks. Social sciences Network analysis. Sociometry. |
| title | Graph theoretic approaches for analyzing large-scale social networks |
| title_auth | Graph theoretic approaches for analyzing large-scale social networks |
| title_exact_search | Graph theoretic approaches for analyzing large-scale social networks |
| title_full | Graph theoretic approaches for analyzing large-scale social networks Natarajan Meghanathan, editor. |
| title_fullStr | Graph theoretic approaches for analyzing large-scale social networks Natarajan Meghanathan, editor. |
| title_full_unstemmed | Graph theoretic approaches for analyzing large-scale social networks Natarajan Meghanathan, editor. |
| title_short | Graph theoretic approaches for analyzing large-scale social networks |
| title_sort | graph theoretic approaches for analyzing large scale social networks |
| topic | Graph theory. Social networks. Social sciences Network analysis. Sociometry. |
| topic_facet | Graph theory. Social networks. Social sciences Network analysis. Sociometry. |
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