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By adaptively fusing low- and high-frequency graph signals based on local anomaly context, SAGAD achieves state-of-the-art graph anomaly detection while scaling linearly to large graphs.
Text-rich networks get a hierarchical upgrade: TIER leverages LLMs and contrastive learning to build taxonomy-aware node embeddings, significantly outperforming existing methods.
Graph-based code representations, largely unexplored in automated patch correctness assessment, crush sequence- and heuristic-based methods, achieving 82.6% accuracy in predicting patch correctness.
