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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sanghavi, Sujay Netrapalli, Praneeth |
| Abstract | We consider the problem of finding the graph on which an epidemic spreads, given only the times when each node gets infected. While this is a problem of central importance in several contexts -- offline and online social networks, e-commerce, epidemiology -- there has been very little work, analytical or empirical, on finding the graph. Clearly, it is impossible to do so from just one epidemic; our interest is in learning the graph from a small number of independent epidemics. For the classic and popular "independent cascade" epidemics, we analytically establish sufficient conditions on the number of epidemics for both the global maximum-likelihood (ML) estimator, and a natural greedy algorithm to succeed with high probability. Both results are based on a key observation: the global graph learning problem decouples into n local problems -- one for each node. For a node of degree d, we show that its neighborhood can be reliably found once it has been infected $O(d^{2}$ log n) times (for ML on general graphs) or O(d log n) times (for greedy on trees). We also provide a corresponding information-theoretic lower bound of Ω(d log n); thus our bounds are essentially tight. Furthermore, if we are given side-information in the form of a super-graph of the actual graph (as is often the case), then the number of epidemic samples required -- in all cases -- becomes independent of the network size n. |
| Starting Page | 211 |
| Ending Page | 222 |
| Page Count | 12 |
| File Format | |
| ISSN | 01635999 |
| DOI | 10.1145/2318857.2254783 |
| Journal | ACM SIGMETRICS Performance Evaluation Review (PERV) |
| Volume Number | 40 |
| Issue Number | 1 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-01-10 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Epidemics Graph structure learning Cascades |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software |
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