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.
In: Proceedings of the 25th ACM SIGKDD inter- national conference on knowledge discovery & data mining
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A distillation technique embeds LLM-generated textual user profiles into efficient sequential recommenders without runtime LLM inference, architectural changes, or fine-tuning.
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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.
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Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
A distillation technique embeds LLM-generated textual user profiles into efficient sequential recommenders without runtime LLM inference, architectural changes, or fine-tuning.