  Recently, graph (network) data is an emerging research area in artificial
intelligence, machine learning and statistics. In this work, we are interested
in whether node's labels (people's responses) are affected by their neighbor's
features (friends' characteristics). We propose a novel latent logistic
regression model to describe the network dependence with binary responses. The
key advantage of our proposed model is that a latent binary indicator is
introduced to indicate whether a node is susceptible to the influence of its
neighbour. A score-type test is proposed to diagnose the existence of network
dependence. In addition, an EM-type algorithm is used to estimate the model
parameters under network dependence. Extensive simulations are conducted to
evaluate the performance of our method. Two public datasets are used to
illustrate the effectiveness of the proposed latent logistic regression model.
