Transfer Learning for Latent Variable Network Models
This work addresses transfer learning for network models, enabling more efficient estimation in data-scarce scenarios, though it appears incremental with specific theoretical and empirical contributions.
The authors tackled the problem of estimating a target latent variable network model with limited data by leveraging a related source network, showing that vanishing error is possible when latent variables are shared, and their algorithm achieves o(1) error without parametric assumptions.
We study transfer learning for estimation in latent variable network models. In our setting, the conditional edge probability matrices given the latent variables are represented by $P$ for the source and $Q$ for the target. We wish to estimate $Q$ given two kinds of data: (1) edge data from a subgraph induced by an $o(1)$ fraction of the nodes of $Q$, and (2) edge data from all of $P$. If the source $P$ has no relation to the target $Q$, the estimation error must be $Ω(1)$. However, we show that if the latent variables are shared, then vanishing error is possible. We give an efficient algorithm that utilizes the ordering of a suitably defined graph distance. Our algorithm achieves $o(1)$ error and does not assume a parametric form on the source or target networks. Next, for the specific case of Stochastic Block Models we prove a minimax lower bound and show that a simple algorithm achieves this rate. Finally, we empirically demonstrate our algorithm's use on real-world and simulated graph transfer problems.