  Multiview network embedding aims at projecting nodes in the network to
low-dimensional vectors, while preserving their multiple relations and
attribute information. Contrastive learning approaches have shown promising
performance in this task. However, they neglect the semantic consistency
between fused and view representations and have difficulty in modeling
complementary information between different views. To deal with these
deficiencies, this work presents a novel Contrastive leaRning framEwork for
Multiview network Embedding (CREME). In our work, different views can be
obtained based on the various relations among nodes. Then, we generate view
embeddings via proper view encoders and utilize an attentive multiview
aggregator to fuse these representations. Particularly, we design two
collaborative contrastive objectives, view fusion InfoMax and inter-view
InfoMin, to train the model in a self-supervised manner. The former objective
distills information from embeddings generated from different views, while the
latter captures complementary information among views to promote distinctive
view embeddings. We also show that the two objectives can be unified into one
objective for model training. Extensive experiments on three real-world
datasets demonstrate that our proposed CREME is able to consistently outperform
state-of-the-art methods.
