Consider a social network where only a few nodes (agents)
have meaningful interactions in the sense that the conditional
dependency graph over node attribute variables (behaviors)
is sparse. A company that can only observe the interactions
between its own customers will generally not be able to ac-
curately estimate its customers’ dependency subgraph: it is
blinded to any external interactions of its customers and this
blindness creates false edges in its subgraph. In this paper
we address the semiblind scenario where the company has
access to a noisy summary of the complementary subgraph
connecting external agents, e.g., provided by a consolidator.
The proposed framework applies to other applications as well,
including field estimation from a network of awake and sleep-
ing sensors and privacy-constrained information sharing over
social subnetworks. We propose a penalized likelihood ap-
proach in the context of a graph signal obeying a Gaussian
graphical models (GGM). We use a convex-concave iterative
optimization algorithm to maximize the penalized likelihood.
The effectiveness of our approach is demonstrated through
numerical experiments and comparison with state-of-the-art
GGM and latent-variable (LV-GGM) methods.