Network Data Mining and Analysis
Nonfiction, Computers, Database Management, Networking & Communications, General Computing
| Author: | Ming Gao, Ee-Peng Lim, David Lo | ISBN: | 9789813274976 |
| Publisher: | World Scientific Publishing Company | Publication: | September 27, 2018 |
| Imprint: | WSPC | Language: | English |
| Author: | Ming Gao, Ee-Peng Lim, David Lo |
| ISBN: | 9789813274976 |
| Publisher: | World Scientific Publishing Company |
| Publication: | September 27, 2018 |
| Imprint: | WSPC |
| Language: | English |
Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day.
To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following:
- What are social communities in bipartite graphs and signed graphs?
- How robust are the networks? How can we apply the robustness of networks?
- How can we find identical social users across heterogeneous social networks?
Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data.
Contents:
- Introduction to Social Networks
- Network Modeling
- R-energy for Evaluating Robustness of Dynamic Networks
- Network Linkage Across Heterogeneous Networks
- Quasi-biclique Detection from Bipartite Graphs
- On Detecting Antagonistic Community Detection from Signed Graphs
- Summary
Readership: Graduate students and researchers seeking more efficient methods to process varying queries in large-scale key-value store networks.
Key Features:
- We address the following latest and key questions as following:
- What are social communities in bipartite graphs and signed graphs?
- How robust the networks are? How to use the robustness of networks?
- How can we find identical social users across heterogeneous social networks?
Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day.
To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following:
- What are social communities in bipartite graphs and signed graphs?
- How robust are the networks? How can we apply the robustness of networks?
- How can we find identical social users across heterogeneous social networks?
Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data.
Contents:
- Introduction to Social Networks
- Network Modeling
- R-energy for Evaluating Robustness of Dynamic Networks
- Network Linkage Across Heterogeneous Networks
- Quasi-biclique Detection from Bipartite Graphs
- On Detecting Antagonistic Community Detection from Signed Graphs
- Summary
Readership: Graduate students and researchers seeking more efficient methods to process varying queries in large-scale key-value store networks.
Key Features:
- We address the following latest and key questions as following:
- What are social communities in bipartite graphs and signed graphs?
- How robust the networks are? How to use the robustness of networks?
- How can we find identical social users across heterogeneous social networks?
