Link prediction for egocentrically sampled networks
This addresses link prediction for social networks with egocentric sampling, an incremental improvement over methods assuming random missing links.
The paper tackles link prediction in networks collected via egocentric sampling, where data are incomplete due to row-wise sampling, and proposes a new algorithm that estimates the underlying probability matrix by focusing on its row space, outperforming existing methods in this specific scenario.
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.