Network data mining and analysis
Publication Type
Book
Publication Date
1-2019
Abstract
Consider an online social networking site with millions of members in which members have the opportunity to befriend one another, send messages to each other, and post content on the site. Facebook, LinkedIn, and Twitter are examples of such sites. To make sense of data from these sites, we resort to social media mining to answer the following questions: 1. What are social communities in bipartite graphs and signed graphs? 2. How robust are the networks? How can we apply the robustness of networks? 3. 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
Keywords
Network modeling, social networks, social media, data mining
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Research Areas
Data Science and Engineering
Volume
8
First Page
1
Last Page
185
ISBN
9789813274952
Identifier
10.1142/11120
Publisher
World Scientific
City or Country
Singapore
Citation
GAO, Ming; LIM, Ee-peng; and LO, David.
Network data mining and analysis. (2019). 8, 1-185.
Available at: https://ink.library.smu.edu.sg/sis_research/4930
Additional URL
https://doi.org/10.1142/11120