Polynomial-time max-product algorithms for exact (neuron-level) and approximate (node-level) top-K relevant walk search in GNN-LRP explanations.
Kipf and Max Welling , title =
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Message-passing algorithms compute GNN-LRP subgraph attributions in linear time w.r.t. network depth by exploiting the distributive property.
GASim accelerates hybrid LLM-ABM social simulations via graph-optimized memory, graph message passing, and entropy-driven agent grouping, delivering 9.94x speedup and under 20% token use while aligning with real-world trends.
citing papers explorer
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Relevant Walk Search for Explaining Graph Neural Networks
Polynomial-time max-product algorithms for exact (neuron-level) and approximate (node-level) top-K relevant walk search in GNN-LRP explanations.
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Efficient Higher-order Subgraph Attribution via Message Passing
Message-passing algorithms compute GNN-LRP subgraph attributions in linear time w.r.t. network depth by exploiting the distributive property.
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GASim: A Graph-Accelerated Hybrid Framework for Social Simulation
GASim accelerates hybrid LLM-ABM social simulations via graph-optimized memory, graph message passing, and entropy-driven agent grouping, delivering 9.94x speedup and under 20% token use while aligning with real-world trends.