DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion
To address these challenges, we propose DeKoR, an efficient framework for GNN and LLM knowledge fusion.
ProofPaper ↗
Key points
- Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language understanding capabilities.
- Combining LLMs with Graph Neural Networks (GNNs) offers a promising paradigm for expanding the modeling capabilities of graph learning.
- To enable selective knowledge fusion, an adaptive router learns how much each node should rely on each expert.
- Extensive experiments on multiple benchmarks demonstrate that DeKoR outperforms existing baselines on both node and subgraph classification, achieving superior effectiveness and efficiency.
Sources (1)
- [1]DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM FusionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:28 AM
To address these challenges, we propose DeKoR, an efficient framework for GNN and LLM knowledge fusion.
Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language understanding capabilities.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 10, 2026LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding
- Oct 10, 2026UltraBERT: Beyond the Limit of BERT Pre-Training
- Oct 10, 2026ProgAgent: Learning Tool Orchestration through Programmatic Training
- Oct 10, 2026Vela: User Behavior as Supervision Sparks LLM Intelligence
- Oct 9, 2026Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding
- Oct 9, 2026ClawLoop: Self-Improving Agents via Harness-Native Training Loop