  In a social network, adoption probability refers to the probability that a
social entity will adopt a product, service, or opinion in the foreseeable
future. Such probabilities are central to fundamental issues in social network
analysis, including the influence maximization problem. In practice, adoption
probabilities have significant implications for applications ranging from
social network-based target marketing to political campaigns; yet, predicting
adoption probabilities has not received sufficient research attention. Building
on relevant social network theories, we identify and operationalize key factors
that affect adoption decisions: social influence, structural equivalence,
entity similarity, and confounding factors. We then develop the
locally-weighted expectation-maximization method for Na\"ive Bayesian learning
to predict adoption probabilities on the basis of these factors. The principal
challenge addressed in this study is how to predict adoption probabilities in
the presence of confounding factors that are generally unobserved. Using data
from two large-scale social networks, we demonstrate the effectiveness of the
proposed method. The empirical results also suggest that cascade methods
primarily using social influence to predict adoption probabilities offer
limited predictive power, and that confounding factors are critical to adoption
probability predictions.
