Tag: Causal Graph Neural Networks
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CD-GNN: Causal Learning for Heterophilic Graphs
Discover how CD-GNN leverages causal disentanglement to improve accuracy in heterophilic graph learning by addressing spurious inductive subgraphs.
Multi-Level Temporal Graph Networks for Industrial Fault Diagnosis
Discover how multi-level temporal graph networks with local-global fusion enhance industrial fault diagnosis by capturing complex sensor relationships.
GLOW: Hybrid LLM-GNN System for Open-World KGQA
Discover GLOW, a hybrid LLM-GNN system enhancing open-world question answering over incomplete knowledge graphs with superior reasoning and accuracy.
Clean-Label Backdoor Attacks on Graph Neural Networks
Explore BA-Logic, a novel method to poison inner prediction logic of GNNs for effective clean-label backdoor attacks in machine learning security.
GTCN-G: Advanced Network Intrusion Detection for Imbalanced Data
Discover GTCN-G, a fusion of graph and temporal networks enhancing intrusion detection in imbalanced datasets for superior cybersecurity.