Multi-view dual-channel graph convolutional networks with multi-task learning

Network embedding has been extensively used in several practical applications and achieved great success. However, existing studies mainly focus on single task or single view and cannot obtain deeper relevant information for accomplishing tasks. In this paper, a novel approach is proposed to address the problem of insufficient information consideration in network embedding, which is termed multi-task-oriented adaptive dual-channel graph convolutional network (TAD-GCN). We firstly use kNN graph construction method to generate three views for each network dataset. Then, the proposed TAD-GCN contains dual-channel GCN which can extract the specific and shared embeddings from multiple views simultaneously, and attention mechanism is adopted to fuse them adaptively. In addition, we design similarity constraint and difference constraint to further enhance their semantic similarity and ensure that they capture the different information. Lastly, a multi-task learning module is introduced to solve multiple tasks simultaneously and optimize the model with its losses. The experimental results demonstrate that our model TAD-GCN not only completes multiple downstream tasks at the same time, but also achieves excellent performance compared with eight state-of-the-art methods.