技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
self-supervised-learning【数据与研究】Use DIG's graph self-supervised learning stack for GraphCL, GRACE, InfoGraph, MVGRL, NodeMVGRL, pGRACE, contrastive objectives, and TUDataset/Planetoid-based SSL evaluation.
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Use this sub-skill for DIG workflows that pretrain graph encoders with contrastive or mutual-information objectives.
dig.sslgraph.method: Contrastive, GraphCL, GRACE, InfoGraph, MVGRL, NodeMVGRL, pGRACE.dig.sslgraph.method.contrastive.views_fn: NodeAttrMask, EdgePerturbation, Diffusion, DiffusionWithSample, UniformSample, RWSample, RandomView, Sequential, AdaEdgePerturbation, AdaNodeAttrMask.dig.sslgraph.method.contrastive.objectives: NCE_loss, JSE_loss.dig.sslgraph.dataset: TUDatasetExt, get_dataset, get_node_dataset.dig.sslgraph.evaluation: GraphSemisupervised, GraphUnsupervised, NodeUnsupervised.dig.sslgraph.utils: Encoder, setup_seed.references/api-reference.md for the constructor and objective names.references/workflows.md for graph-level and node-level SSL workflows.references/troubleshooting.md when augmentations, labels, or datasets are misconfigured.scripts/sslgraph_smoke.py for a tiny CPU-only pretraining smoke check.get_dataset(..., task='semisupervised'|'unsupervised'), build an Encoder, choose GraphCL/GRACE/InfoGraph/MVGRL, pretrain, and evaluate with GraphUnsupervised or GraphSemisupervised.get_node_dataset, build node-level encoders, choose GRACE or NodeMVGRL, and evaluate with NodeUnsupervised.NodeAttrMask, EdgePerturbation, Diffusion, or RandomView and pass them into Contrastive directly.python scripts/sslgraph_smoke.py --help
python scripts/sslgraph_smoke.py