Feature selection-based rough graph classification via graph convolutional neural networks

In various applications, graph data is usually omnipresent. Many existing methods integrate graph classification and feature selection to select informative features; however, uncertainty in graph data is easily ignored and then the relationship between nodes has been not described fully. Additionally, when processing graph data, graph convolutional neural networks (GCNs) fall short in illustrating the structural features and inherent semantic of whole graphs, and relying on node features will lead to over-smoothing, which can affect the efficacy of graph classification. To address these flaws, this article presents a novel feature selection-based rough graph classification methodology via GCNs. Firstly, the average value of each node feature values is calculated to be sorted. A variance filtering-based feature selection scheme is constructed to remove irrelevant node features. Then, the upper approximate and lower approximate feature sets will be divided by a variance threshold. Secondly, according to the relationship between the upper and lower approximates, the upper approximate weight and lower approximate weight of nodes are provided, and the upper approximate weight and lower approximate weight of edges between nodes can be provided to remove the influence of redundant information on nodes. The weight coefficient between nodes is learned by calculating the similarity between nodes to create the upper and lower approximation graphs, and then rough graphs will be constructed to fully display the uncertainty between nodes. Thirdly, an updated self-attention pooling layer scheme is presented to evaluate the importance of nodes, GraphConv is used to extract features for an output feature matrix and obtain a sequence of important nodes. Feature matrix and adjacency matrix can be updated, the hierarchical pooling structure is studied to retain important nodes in network training, and then a simplified rough graph can be obtained. Thus, an end-to-end GCN model is designed by simultaneously considering the structure and node features of rough graphs. Finally, a novel rough graph classification algorithm via GCNs is designed. Experiments on 15 graph datasets illustrate the greater effectiveness of the designed methodology relative to other competitive algorithms.