Efficient Online Summarization of Large-Scale Dynamic Networks
文献类型：期刊论文
| 作者 | Feida Zhu; Siyuan Liu; Qiang Qu; Christian S. Jensen |
| 刊名 | IEEE Transactions on Knowledge and Data Engineering
 |
| 出版日期 | 2016 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Information diffusion in social networks is often characterized by huge participating communities and viral cascades of high dynamicity. To observe, summarize, and understand the evolution of dynamic diffusion processes in an informative and insightful way is a challenge of high practical value. However, few existing studies aim to summarize networks for interesting dynamic patterns. Dynamic networks raise new challenges not found in static settings, including time sensitivity, online interestingness evaluation, and summary traceability, which render existing techniques inadequate. We propose dynamic network summarization to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges overtime. 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. Efficient algorithms are included in OSNet. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness, efficiency, and design properties of OSNet. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11923]  |
| 专题 | 深圳先进技术研究院_其他 |
| 作者单位 | IEEE Transactions on Knowledge and Data Engineering |
| 推荐引用方式 GB/T 7714 | Feida Zhu,Siyuan Liu,Qiang Qu,et al. Efficient Online Summarization of Large-Scale Dynamic Networks[J]. IEEE Transactions on Knowledge and Data Engineering,2016. |
| APA | Feida Zhu,Siyuan Liu,Qiang Qu,&Christian S. Jensen.(2016).Efficient Online Summarization of Large-Scale Dynamic Networks.IEEE Transactions on Knowledge and Data Engineering. |
| MLA | Feida Zhu,et al."Efficient Online Summarization of Large-Scale Dynamic Networks".IEEE Transactions on Knowledge and Data Engineering (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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