Models regimes in temporal graphs as geodesic trajectories and detects changes as drifts from estimated geodesics, outperforming baselines on synthetic data and showing better alignment with external events on COVID mobility data.
Laplacian Change Point Detection for Dynamic Graphs,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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UNVERDICTED 2representative citing papers
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
citing papers explorer
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Geodesics of Dynamic Graphs for Regime Change Detection
Models regimes in temporal graphs as geodesic trajectories and detects changes as drifts from estimated geodesics, outperforming baselines on synthetic data and showing better alignment with external events on COVID mobility data.
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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.