  Link prediction in networks is typically accomplished by estimating or
ranking the probabilities of edges for all pairs of nodes. In practice,
especially for social networks, the data are often collected by egocentric
sampling, which means selecting a subset of nodes and recording all of their
edges. This sampling mechanism requires different prediction tools than the
typical assumption of links missing at random. We propose a new computationally
efficient link prediction algorithm for egocentrically sampled networks, which
estimates the underlying probability matrix by estimating its row space. For
networks created by sampling rows, our method outperforms many popular link
prediction and graphon estimation techniques.
