Consider a 0-1 observation matrix M , where rows correspond 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 finding which entries in the matrix are initiators. We formally define 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 field of socialnetwork analysis. We apply our method to paleontological and ecological data and show that our algorithms work well in practice and give reasonable results.
In REF the authors try to infer the links between individuals given their feature vectors.
Finding links and initiators: a graph reconstruction problem
ArXiv
ArXiv
Computer Science Physics