Mining Diversity on Social Media Networks

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis h...

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Main Authors: LIU, Lu, ZHU, Feida, JIANG, Meng, Han, Jiawei, SUN, Lifeng, YANG, Shiqiang
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/1351
https://ink.library.smu.edu.sg/context/sis_research/article/2350/viewcontent/Mining_diversity_social_2012_av.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-23502020-01-20T05:18:47Z Mining Diversity on Social Media Networks LIU, Lu ZHU, Feida JIANG, Meng Han, Jiawei SUN, Lifeng YANG, Shiqiang The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. Based on the approach, we can measure not only a user’s sociality and interest diversity but also a social media’s user diversity. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive. 2012-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1351 info:doi/10.1007/s11042-010-0568-1 https://ink.library.smu.edu.sg/context/sis_research/article/2350/viewcontent/Mining_diversity_social_2012_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Social network Mining Diversity Communication Technology and New Media Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Social network
Mining
Diversity
Communication Technology and New Media
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Social network
Mining
Diversity
Communication Technology and New Media
Databases and Information Systems
Numerical Analysis and Scientific Computing
LIU, Lu
ZHU, Feida
JIANG, Meng
Han, Jiawei
SUN, Lifeng
YANG, Shiqiang
Mining Diversity on Social Media Networks
description The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. Based on the approach, we can measure not only a user’s sociality and interest diversity but also a social media’s user diversity. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive.
format text
author LIU, Lu
ZHU, Feida
JIANG, Meng
Han, Jiawei
SUN, Lifeng
YANG, Shiqiang
author_facet LIU, Lu
ZHU, Feida
JIANG, Meng
Han, Jiawei
SUN, Lifeng
YANG, Shiqiang
author_sort LIU, Lu
title Mining Diversity on Social Media Networks
title_short Mining Diversity on Social Media Networks
title_full Mining Diversity on Social Media Networks
title_fullStr Mining Diversity on Social Media Networks
title_full_unstemmed Mining Diversity on Social Media Networks
title_sort mining diversity on social media networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/1351
https://ink.library.smu.edu.sg/context/sis_research/article/2350/viewcontent/Mining_diversity_social_2012_av.pdf
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