Graph convolutional networks (GCN) have been widely used in processing graphs and networks data. However, some recent research experiments show that the existing graph convolutional networks have isseus when integrating node features and topology structure. In order to remedy the weakness, we propose a new GCN architecture. Firstly, the proposed architecture introduces the cross-stitch networks into GCN with improved cross-stitch units. Cross-stitch networks spread information/knowledge between node features and topology structure, and obtains consistent learned representation by integrating information of node features and topology structure at the same time. Therefore, the proposed model can capture various channel information in all images through multiple channels. Secondly, an attention mechanism is to further extract the most relevant information between channel embeddings. Experiments on six benchmark datasets shows that our method outperforms all comparison methods on different evaluation indicators.

Hierarchical graph pooling has shown great potential for capturing high-quality graph representations through the node cluster selection mechanism. However, the current node cluster selection methods have inadequate clustering issues, and their scoring methods rely too much on the node representation, resulting in excessive graph structure information loss during pooling. In this paper, a local capsule pooling network (LCPN) is proposed to alleviate the above issues. Specifically, (i) a local capsule pooling (LCP) is proposed to alleviate the issue of insufficient clustering; (ii) a task-aware readout (TAR) mechanism is proposed to obtain a more expressive graph representation; (iii) a pooling information loss (PIL) term is proposed to further alleviate the information loss caused by pooling during training. Experimental results on the graph classification task, the graph reconstruction task, and the pooled graph adjacency visualization task show the superior performance of the proposed LCPN and demonstrate its effectiveness and efficiency.