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A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection
However, most existing graph pooling strategies rely on an assignment matrix obtained by employing a GNN layer, which is characterized by trainable parameters, often leading to significant computational complexity and a lack of interpretability in the pooling process.
Anisotropic Graph Convolutional Network for Semi-supervised Learning
Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification.
Graph Fairing Convolutional Networks for Anomaly Detection
The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
