Graph Auto

Graph auto-encoders (GAEs) are neural network models designed to learn low-dimensional representations of graph-structured data by encoding and decoding graph information. Current research focuses on improving GAE performance through architectural innovations, such as incorporating hierarchical clustering, disentangled representations, and neighborhood reconstruction, to address limitations like over-smoothing and the inability to capture complex structural anomalies. These advancements enhance the effectiveness of GAEs in various applications, including node classification, link prediction, anomaly detection, and medical image analysis, by providing more robust and interpretable graph embeddings.