Adaptive symbiotic graph convolutional network

Abstract

Within the intrinsic fabric of graph and network data, the latent reciprocity between network nodes forms a profound symbiotic relationship. Traditional graph neural networks struggle to adaptively parse and integrate multi-source features when learning these symbiotic relationships. To address this challenge, we propose a novel Adaptive Symbiotic Graph Convolutional Network (ASGCN) for semi-supervised node classification tasks. First, The initial contribution of this investigation is the introduction of a multi-scale feature convolution module, which enables the extraction of hierarchical features at varying scales and the construction of k-nearest neighbor graphs. This facilitates the deepening of the symbiotic features of nodes. Second, a symbiotic co-convolution module is put forth as a means of reinforcing the profound interdependence inherent to symbiotic relationships. Finally, an adaptive dynamic feature selection mechanism is introduced to flexibly respond to data characteristics, effectively identifying and fusing the most influential features in the processing information flow. Experimental results demonstrate that ASGCN exhibits significant advantages in deeply analyzing and integrating the intrinsic attributes of nodes with graph structural relationships, thereby improving performance in node classification tasks.