3 accepted papers
This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs,…
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks. However, the existing studies mostly only consider local connectivi…
This study aims to represent stories in narrative works (i.e., creative works that contain stories) with a fixed-length vector. We apply subgraph-based graph embedding models to dynamic social networks of characters that appeared in stories (character networks). We suppose that interactions between…