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Found 6 papers, 6 papers with code
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
In this work, we propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization
However, we argue that identifying the edges from the invariant subgraph directly is challenging and error-prone, especially when some spurious edges exhibit strong correlations with the targets.
Empowering Graph Invariance Learning with Deep Spurious Infomax
To bridge this gap, we introduce a novel graph invariance learning paradigm, which induces a robust and general inductive bias.
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
To tackle the second challenge, MuGSI proposes to incorporate a node feature augmentation component, thereby enhancing the expressiveness of the student MLPs and making them more capable learners.
Improving the Expressiveness of $K$-hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Recently, there is a line of works aiming to enhance the expressive power of graph neural networks.
Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization
Our approach relaxes the discrete jailbreak optimization into a continuous optimization and progressively increases the sparsity of the optimizing vectors.
