How does research evolve? Pattern mining for research meme cycles

He, D; Zhu, X; Parker, DS

How does research evolve? Pattern mining for research meme cycles

He, D Zhu, X Parker, DS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 1068 - 1073
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	He, D	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Parker, DS	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 1068 - 1073	en_US
dc.identifier.isbn	9780769544083	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19193
dc.description.abstract	Recent years have witnessed a great deal of attention in tracking news memes over the web, modeling shifts in the ebb and flow of their popularity. One of the most important features of news memes is that they seldom occur repeatedly; instead, they tend to shift to different but similar memes. In this work, we consider patterns in research memes, which differ significantly from news memes and have received very little attention. One significant difference between research memes and news memes lies in that research memes have cyclic development, motivating the need for models of cycles of research memes. Furthermore, these cycles may reveal important patterns of evolving research, shedding lights on how research progresses. In this paper, we formulate the modeling of the cycles of research memes, and propose solutions to the problem of identifying cycles and discovering patterns among these cycles. Experiments on two different domain applications indicate that our model does find meaningful patterns and our algorithms for pattern discovery are efficient for large scale data analysis. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2011.76	en_US
dc.title	How does research evolve? Pattern mining for research meme cycles	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Vancouver, BC	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recent years have witnessed a great deal of attention in tracking news memes over the web, modeling shifts in the ebb and flow of their popularity. One of the most important features of news memes is that they seldom occur repeatedly; instead, they tend to shift to different but similar memes. In this work, we consider patterns in research memes, which differ significantly from news memes and have received very little attention. One significant difference between research memes and news memes lies in that research memes have cyclic development, motivating the need for models of cycles of research memes. Furthermore, these cycles may reveal important patterns of evolving research, shedding lights on how research progresses. In this paper, we formulate the modeling of the cycles of research memes, and propose solutions to the problem of identifying cycles and discovering patterns among these cycles. Experiments on two different domain applications indicate that our model does find meaningful patterns and our algorithms for pattern discovery are efficient for large scale data analysis. © 2011 IEEE.

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http://hdl.handle.net/10453/19193