Anime Link Prediction Using Improved Graph Convolutional Networks

Graph convolutional neural networks (GCNs) have made significant strides in recent years including social network analysis, recommendation systems, drug discovery, and bioinformatics. This study introduces an updated graph convolutional anime dataset. To improve the performance of GCNs on anime datasets, we present a novel method that combines the advantages of existing graph neural network architectures like Graph Attention Networks (GAT) and Spectral Aggregated Graph Embedding (GraphSage). To perform better in tasks requiring anime analysis, our suggested solution makes use of a cutting-edge algorithm. We outline our experimental findings and then offer some last thoughts on how our modified GCN might be used.