Network data mining and analysis

• Main Authors: GAO, Ming, LIM, Ee-peng, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Network modeling; social networks; social media; data mining; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4930
• Institution: Singapore Management University

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