A0398
Title: Fitting low-rank models on egocentrically sampled partial networks
Authors: Tianxi Li - University of Minnesota (United States) [presenting]
Abstract: The statistical modelling of random networks has been widely used to uncover complex system interaction mechanisms and predict unobserved links in real-world networks. In many applications, network connections are collected via egocentric sampling: a subset of nodes is sampled first, after which all links involving this subset are recorded; all other information is missing. Compared with the assumption of "uniformly missing at random", egocentrically sampled partial networks require specially designed modelling strategies. Current statistical methods are either computationally infeasible or based on intuitive designs without theoretical justification. Here, an approach is proposed to fit general low-rank models for egocentrically sampled networks, which include several popular network models. This method is based on graph spectral properties and is computationally efficient for large-scale networks. It results in consistent recovery of missing subnetworks due to egocentric sampling for sparse networks. To our knowledge, this method offers the first theoretical guarantee for egocentric partial network estimation in the scope of low-rank models. The technique is evaluated on several synthetic and real-world networks, and it is shown that it delivers competitive performance in link prediction tasks.
