Graph Completion

Graph completion aims to reconstruct missing information—nodes, edges, or features—within a graph, improving the accuracy and completeness of graph-based analyses. Current research focuses on developing sophisticated graph neural network (GNN) architectures, often incorporating self-supervised learning or knowledge enhancement techniques, to effectively infer missing data. These methods are applied across diverse domains, including knowledge graphs, social networks, and multimodal data like conversations, enabling more robust analysis and improved performance in downstream tasks such as node classification and link prediction. The resulting advancements have significant implications for various fields relying on graph data, enhancing the reliability and utility of analyses based on incomplete or noisy information.