GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
Boost then convolve: Gradient boosting meets graph neural networks
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ReFi-GAD uses a semantics-aware relational fingerprint and transformer-based model with SNR refinement to align heterogeneous features for generalist graph anomaly detection across unseen graphs.
AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
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Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
ReFi-GAD uses a semantics-aware relational fingerprint and transformer-based model with SNR refinement to align heterogeneous features for generalist graph anomaly detection across unseen graphs.
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A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.