Node Representation

Node representation learning aims to encode graph nodes into low-dimensional vector embeddings that capture both node features and structural context, facilitating downstream tasks like node classification and link prediction. Current research emphasizes improving embedding quality through techniques like contrastive learning, addressing challenges such as over-smoothing in graph neural networks (GNNs) and handling class imbalances, often employing GNN architectures or multi-layer perceptrons (MLPs) with various enhancements. These advancements are significant for improving the performance and robustness of graph-based machine learning models across diverse applications, including anomaly detection, recommendation systems, and scientific knowledge discovery.