Interestingness-Driven Diffussion Process Summarization in Dynamic Networks

The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key to...

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Main Authors: Qu, Qiang, Liu, Siyuan, Jensen, Christian, ZHU, Feida, Faloutsos, Christos
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2651
https://ink.library.smu.edu.sg/context/sis_research/article/3651/viewcontent/C117___Interestingness_Driven_Diffusion_Process_Summarization_in_Dynamic_Networks__PKDD14_.pdf
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spelling sg-smu-ink.sis_research-36512018-07-13T04:17:14Z Interestingness-Driven Diffussion Process Summarization in Dynamic Networks Qu, Qiang Liu, Siyuan Jensen, Christian ZHU, Feida Faloutsos, Christos The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose OSNet, an online summarization framework for dynamic networks. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness and design properties ofOSNet. 2014-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2651 info:doi/10.1007/978-3-662-44851-9_38 https://ink.library.smu.edu.sg/context/sis_research/article/3651/viewcontent/C117___Interestingness_Driven_Diffusion_Process_Summarization_in_Dynamic_Networks__PKDD14_.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 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 Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
Qu, Qiang
Liu, Siyuan
Jensen, Christian
ZHU, Feida
Faloutsos, Christos
Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
description The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose OSNet, an online summarization framework for dynamic networks. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness and design properties ofOSNet.
format text
author Qu, Qiang
Liu, Siyuan
Jensen, Christian
ZHU, Feida
Faloutsos, Christos
author_facet Qu, Qiang
Liu, Siyuan
Jensen, Christian
ZHU, Feida
Faloutsos, Christos
author_sort Qu, Qiang
title Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
title_short Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
title_full Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
title_fullStr Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
title_full_unstemmed Interestingness-Driven Diffussion Process Summarization in Dynamic Networks
title_sort interestingness-driven diffussion process summarization in dynamic networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2651
https://ink.library.smu.edu.sg/context/sis_research/article/3651/viewcontent/C117___Interestingness_Driven_Diffusion_Process_Summarization_in_Dynamic_Networks__PKDD14_.pdf
_version_ 1770572537984450560