  Link prediction and node classification are two important downstream tasks of
network representation learning. Existing methods have achieved acceptable
results but they perform these two tasks separately, which requires a lot of
duplication of work and ignores the correlations between tasks. Besides,
conventional models suffer from the identical treatment of information of
multiple views, thus they fail to learn robust representation for downstream
tasks. To this end, we tackle link prediction and node classification problems
simultaneously via multi-task multi-view learning in this paper. We first
explain the feasibility and advantages of multi-task multi-view learning for
these two tasks. Then we propose a novel model named as MT-MVGCN to perform
link prediction and node classification tasks simultaneously. More
specifically, we design a multi-view graph convolutional network to extract
abundant information of multiple views in a network, which is shared by
different tasks. We further apply two attention mechanisms: view attention
mechanism and task attention mechanism to make views and tasks adjust the view
fusion process. Moreover, view reconstruction can be introduced as an auxiliary
task to boost the performance of the proposed model. Experiments on real-world
network datasets demonstrate that our model is efficient yet effective, and
outperforms advanced baselines in these two tasks.
