U-STS-LLM uses a spatio-temporally steered LLM with dynamic attention bias generation to achieve state-of-the-art results on long-horizon traffic forecasting and high-missing-rate imputation while remaining parameter-efficient.
Graphgpt: Graph instruction tuning for large language models
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UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
LoReC enhances LLMs for graph tasks via attention redistribution, graph re-injection into FFN, and logit rectification, yielding improvements over GraphLLM and GNN baselines on diverse datasets.
citing papers explorer
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U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation
U-STS-LLM uses a spatio-temporally steered LLM with dynamic attention bias generation to achieve state-of-the-art results on long-horizon traffic forecasting and high-missing-rate imputation while remaining parameter-efficient.
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A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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LoReC: Rethinking Large Language Models for Graph Data Analysis
LoReC enhances LLMs for graph tasks via attention redistribution, graph re-injection into FFN, and logit rectification, yielding improvements over GraphLLM and GNN baselines on diverse datasets.