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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Zong Yinghui Wu Singh, A.K. Xifeng Yan |
| Copyright Year | 2012 |
| Abstract | In social networks, information and influence diffuse among users as cascades. While the importance of studying cascades has been recognized in various applications, it is difficult to observe the complete structure of cascades in practice. In this paper we study the cascade inference problem following the independent cascade model, and provide a full treatment from complexity to algorithms: (a) we propose the idea of consistent trees as the inferred structures for cascades, these trees connect source nodes and observed nodes with paths satisfying the constraints from the observed temporal information. (b) We introduce metrics to measure the likelihood of consistent trees as inferred cascades, as well as several optimization problems for finding them. (c) We show that the decision problems for consistent trees are in general NP-complete, and that the optimization problems are hard to approximate. (d) We provide approximation algorithms with performance guarantees on the quality of the inferred cascades, as well as heuristics. We experimentally verify the efficiency and effectiveness of our inference algorithms, using real and synthetic data. |
| Starting Page | 1218 |
| Ending Page | 1223 |
| File Size | 324470 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467346498 |
| ISSN | 15504786 |
| DOI | 10.1109/ICDM.2012.100 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-12-10 |
| Publisher Place | Belgium |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vegetation Approximation methods Approximation algorithms Inference algorithms Measurement Uncertainty Steiner trees cascade prediction information diffusion |
| Content Type | Text |
| Resource Type | Article |
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