Discovering hidden topical hubs and authorities in online social networks

Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not c...

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Main Authors: LEE, Roy Ka-Wei, HOANG, Tuan-Anh, LIM, Ee-Peng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4081
https://ink.library.smu.edu.sg/context/sis_research/article/5084/viewcontent/Discovering_hidden_topical_hubs_afv_2018.pdf
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spelling sg-smu-ink.sis_research-50842019-06-04T06:05:06Z Discovering hidden topical hubs and authorities in online social networks LEE, Roy Ka-Wei HOANG, Tuan-Anh LIM, Ee-Peng Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state-of-the-art in link recommendation task. 2018-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4081 info:doi/10.1137/1.9781611975321.43 https://ink.library.smu.edu.sg/context/sis_research/article/5084/viewcontent/Discovering_hidden_topical_hubs_afv_2018.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 HITS algorithms Link analysis On-line social networks Real-world datasets State of the art State-of-the-art methods Topic model Topic Modeling 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 HITS algorithms
Link analysis
On-line social networks
Real-world datasets
State of the art
State-of-the-art methods
Topic model
Topic Modeling
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
spellingShingle HITS algorithms
Link analysis
On-line social networks
Real-world datasets
State of the art
State-of-the-art methods
Topic model
Topic Modeling
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
LEE, Roy Ka-Wei
HOANG, Tuan-Anh
LIM, Ee-Peng
Discovering hidden topical hubs and authorities in online social networks
description Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state-of-the-art in link recommendation task.
format text
author LEE, Roy Ka-Wei
HOANG, Tuan-Anh
LIM, Ee-Peng
author_facet LEE, Roy Ka-Wei
HOANG, Tuan-Anh
LIM, Ee-Peng
author_sort LEE, Roy Ka-Wei
title Discovering hidden topical hubs and authorities in online social networks
title_short Discovering hidden topical hubs and authorities in online social networks
title_full Discovering hidden topical hubs and authorities in online social networks
title_fullStr Discovering hidden topical hubs and authorities in online social networks
title_full_unstemmed Discovering hidden topical hubs and authorities in online social networks
title_sort discovering hidden topical hubs and authorities in online social networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4081
https://ink.library.smu.edu.sg/context/sis_research/article/5084/viewcontent/Discovering_hidden_topical_hubs_afv_2018.pdf
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