Name
Abstract | ||
|---|---|---|
Consider a 0{1 observation matrix M, where rows cor- respond to entities and columns correspond to signals; a value of 1 (or 0) in cell (i;j) of M indicates that signal j has been observed (or not observed) in entity i. Given such a matrix we study the problem of inferring the underlying directed links between entities (rows) and flnding which entries in the matrix are initiators. We formally deflne this problem and propose an MCMC framework for estimating the links and the initiators given the matrix of observations M. We also show how this framework can be extended to incorporate a temporal aspect; instead of considering a single observation matrix M we consider a sequence of observation matrices M1;:::;Mt over time. We show the connection between our problem and several problems studied in the fleld of social-network analysis. We apply our method to paleontological and ecological data and show that our algorithms work well in practice and give reasonable results. |
Year | Venue | Keywords |
|---|---|---|
2008 | siam international conference on data mining | social network analysis,artificial intelligent |
DocType | Volume | Citations |
Journal | abs/0809.3027 | 4 |
PageRank | References | Authors |
0.65 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heikki Mannila | 1 | 6595 | 1495.69 |
| Evimaria Terzi | 2 | 1580 | 83.54 |