  Recently, the Network Representation Learning (NRL) techniques, which
represent graph structure via low-dimension vectors to support social-oriented
application, have attracted wide attention. Though large efforts have been
made, they may fail to describe the multiple aspects of similarity between
social users, as only a single vector for one unique aspect has been
represented for each node. To that end, in this paper, we propose a novel
end-to-end framework named MCNE to learn multiple conditional network
representations, so that various preferences for multiple behaviors could be
fully captured. Specifically, we first design a binary mask layer to divide the
single vector as conditional embeddings for multiple behaviors. Then, we
introduce the attention network to model interaction relationship among
multiple preferences, and further utilize the adapted message sending and
receiving operation of graph neural network, so that multi-aspect preference
information from high-order neighbors will be captured. Finally, we utilize
Bayesian Personalized Ranking loss function to learn the preference similarity
on each behavior, and jointly learn multiple conditional node embeddings via
multi-task learning framework. Extensive experiments on public datasets
validate that our MCNE framework could significantly outperform several
state-of-the-art baselines, and further support the visualization and transfer
learning tasks with excellent interpretability and robustness.
