Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
Machine Vision and Applications33(1), 4 (2022)
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NK-GAD improves unsupervised graph anomaly detection on heterophilic graphs by combining a joint encoder for similar and dissimilar neighbors, neighbor reconstruction, center aggregation, and dual decoders, yielding an average 3.29% AUC gain across seven datasets.
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Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
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NK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection
NK-GAD improves unsupervised graph anomaly detection on heterophilic graphs by combining a joint encoder for similar and dissimilar neighbors, neighbor reconstruction, center aggregation, and dual decoders, yielding an average 3.29% AUC gain across seven datasets.