Graph Embeddings

Graph embeddings represent complex graph structures as low-dimensional vectors, aiming to capture essential topological and semantic information for efficient machine learning tasks. Current research focuses on developing more efficient and interpretable embedding methods, including those based on topological data analysis, continuous latent spaces, and manifold learning, as well as integrating graph embeddings with large language models and addressing challenges like scalability and bias mitigation. These advancements are significantly impacting various fields, enabling improved performance in graph-based applications such as node classification, link prediction, and knowledge graph reasoning, as well as facilitating the analysis of large-scale datasets.