Node Similarity

Node similarity focuses on quantifying the resemblance between nodes within a graph, aiming to improve various graph-related tasks like clustering, link prediction, and node classification. Current research emphasizes developing robust methods for measuring node similarity, incorporating both local neighborhood structures and global graph properties, often leveraging graph neural networks (GNNs) and contrastive learning techniques. These advancements are crucial for enhancing the performance and interpretability of graph-based algorithms across diverse applications, including social network analysis, recommendation systems, and biological network modeling.