Network data mining and analysis

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 t...

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Main Authors: GAO, Ming, LIM, Ee-peng, LO, David
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/4930
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spelling sg-smu-ink.sis_research-59332020-02-13T06:24:03Z Network data mining and analysis GAO, Ming LIM, Ee-peng LO, David 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 2019-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/4930 info:doi/10.1142/11120 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Network modeling social networks social media data mining Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Network modeling
social networks
social media
data mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
spellingShingle Network modeling
social networks
social media
data mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
GAO, Ming
LIM, Ee-peng
LO, David
Network data mining and analysis
description 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
format text
author GAO, Ming
LIM, Ee-peng
LO, David
author_facet GAO, Ming
LIM, Ee-peng
LO, David
author_sort GAO, Ming
title Network data mining and analysis
title_short Network data mining and analysis
title_full Network data mining and analysis
title_fullStr Network data mining and analysis
title_full_unstemmed Network data mining and analysis
title_sort network data mining and analysis
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4930
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