  We exploit the core-periphery structure and the strong homophilic properties
of online social networks to develop faster and more accurate algorithms for
user interest prediction. The core of modern social networks consists of
relatively few influential users, whose interest profiles are publicly
available, while the majority of peripheral users follow enough of them based
on common interests. Our approach is to predict the interests of the peripheral
nodes starting from the interests of their influential connections. To this
end, we need a formal model that explains how common interests lead to network
connections. Thus, we propose a stochastic interest formation model, the
Nearest Neighbor Influence Model (NNIM), which is inspired by the
Hegselmann-Krause opinion formation model and aims to explain how homophily
shapes the network. Based on NNIM, we develop an efficient approach for
predicting the interests of the peripheral users. At the technical level, we
use Variational Expectation-Maximization to optimize the instantaneous
likelihood function using a mean-field approximation of NNIM. We prove that our
algorithm converges fast and is capable of scaling smoothly to networks with
millions of nodes. Our experiments on standard network benchmarks demonstrate
that our algorithm runs up to two orders of magnitude faster than the best
known node embedding methods and achieves similar accuracy.
