Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting

arXiv:2402.18697v1 Announce Type: cross
Abstract: A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this problem have repurposed the classic iterative proportional fitting (IPF) procedure, also known as Sinkhorn's algorithm, with promising empirical results. However, the statistical foundation for using IPF has not been well understood: under what settings does IPF provide principled estimation of a …

abstract arxiv column constraints cs.lg cs.si data dynamic inference iterative its time math.oc math.st matrix network networks prior stat.ml stat.th type world