Multi-scale fusion graph convolutional networks

Abstract

Graph analysis methods, as important tools for mining complex information, have made remarkable progress driven by graph neural networks (GNNs). However, existing approaches still face challenges in handling complex topological structures and multi-dimensional node features, making it difficult to fully capture deep-level feature and structural information. When analyzing attribute networks, a key challenge is how to effectively integrate node attribute features with graph topological structure information. To address this issue, this paper proposes a multi-scale fusion graph convolutional network (MSF-GCN) method. This method combines shallow and deep convolution strategies while adaptively fusing information across three parallel channels — the original topological structure, a feature-derived graph, and a deep-combination channel that captures shared depth information between them. An autoencoder is employed to reconstruct the adjacency matrix, enhancing the representation capability of the network. Additionally, an attention mechanism is introduced to dynamically assign weights to attribute and structural features at different scales, optimizing node representation. Experimental results demonstrate that, in node classification tasks across multiple benchmark datasets, MSF-GCN achieves outstanding performance, strongly validating the effectiveness and robustness of the proposed method.