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1. SMoRE-LLM SMoRE-LLM Public

   [NeurIPS'25] Structural Mixture of Residual Experts for Parameter Efficient Fine-Tuning

   Python 1

2. facebookresearch/mowst-gnn facebookresearch/mowst-gnn Public

   [ICLR 2024] heterogeneous MoE: mixture of weak & strong experts on graphs https//openreview.net/pdf?id=wYvuY60SdD

   Python 20 1

3. facebookresearch/shaDow_GNN facebookresearch/shaDow_GNN Public

   [NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architectures

   Python 137 15

4. GraphSAINT/GraphSAINT GraphSAINT/GraphSAINT Public

   [ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).

   Python 500 89

5. facebookresearch/taser-tgnn facebookresearch/taser-tgnn Public

   [IPDPS 2024] Adaptive neighbor sampling for temporal GNN

   Python 16 1

6. GraphSAINT/GNN-ARCH GraphSAINT/GNN-ARCH Public

   [ASAP 2020; FPGA 2020] Hardware architecture to accelerate GNNs (common IP modules for minibatch training and full batch inference)

   Verilog 41 9