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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dan He Xingquan Zhu Parker, D.S. |
| Copyright Year | 2011 |
| 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. |
| Starting Page | 1068 |
| Ending Page | 1073 |
| File Size | 579711 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457720758 |
| ISSN | 15504786 |
| DOI | 10.1109/ICDM.2011.76 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-11 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Computational modeling Heuristic algorithms Complexity theory Data mining Social network services Ontologies topic evolution Research memes frequent patterns MeSH hierarchy shortest paths topic mining |
| Content Type | Text |
| Resource Type | Article |
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