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M-DESIGN-Knowledge-Base

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Upload M-DESIGN knowledge base and model artifacts

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  1. .gitattributes +33 -0
  2. README.md +41 -0
  3. graph/tu_cox2/TU_COX2.db +3 -0
  4. graph/tu_cox2/ecc_predictor.pt +3 -0
  5. graph/tu_cox2/model_graph.pt +3 -0
  6. graph/tu_dd/TU_DD.db +3 -0
  7. graph/tu_dd/ecc_predictor.pt +3 -0
  8. graph/tu_dd/model_graph.pt +3 -0
  9. graph/tu_imdb-binary/TU_IMDB-BINARY.db +3 -0
  10. graph/tu_imdb-binary/ecc_predictor.pt +3 -0
  11. graph/tu_imdb-binary/model_graph.pt +3 -0
  12. graph/tu_imdb-multi/TU_IMDB-MULTI.db +3 -0
  13. graph/tu_imdb-multi/ecc_predictor.pt +3 -0
  14. graph/tu_imdb-multi/model_graph.pt +3 -0
  15. graph/tu_nci1/TU_NCI1.db +3 -0
  16. graph/tu_nci1/ecc_predictor.pt +3 -0
  17. graph/tu_nci1/model_graph.pt +3 -0
  18. graph/tu_nci109/TU_NCI109.db +3 -0
  19. graph/tu_nci109/ecc_predictor.pt +3 -0
  20. graph/tu_nci109/model_graph.pt +3 -0
  21. graph/tu_proteins/TU_PROTEINS.db +3 -0
  22. graph/tu_proteins/ecc_predictor.pt +3 -0
  23. graph/tu_proteins/model_graph.pt +3 -0
  24. graph/tu_ptc_fm/TU_PTC_FM.db +3 -0
  25. graph/tu_ptc_fm/ecc_predictor.pt +3 -0
  26. graph/tu_ptc_fm/model_graph.pt +3 -0
  27. graph/tu_ptc_fr/TU_PTC_FR.db +3 -0
  28. graph/tu_ptc_fr/ecc_predictor.pt +3 -0
  29. graph/tu_ptc_fr/model_graph.pt +3 -0
  30. graph/tu_ptc_mm/TU_PTC_MM.db +3 -0
  31. graph/tu_ptc_mm/ecc_predictor.pt +3 -0
  32. graph/tu_ptc_mm/model_graph.pt +3 -0
  33. graph/tu_ptc_mr/TU_PTC_MR.db +3 -0
  34. graph/tu_ptc_mr/ecc_predictor.pt +3 -0
  35. graph/tu_ptc_mr/model_graph.pt +3 -0
  36. link/actor/Actor.db +3 -0
  37. link/actor/ecc_predictor.pt +3 -0
  38. link/actor/model_graph.pt +3 -0
  39. link/amazoncomputers/AmazonComputers.db +3 -0
  40. link/amazoncomputers/ecc_predictor.pt +3 -0
  41. link/amazoncomputers/model_graph.pt +3 -0
  42. link/amazonphoto/AmazonPhoto.db +3 -0
  43. link/amazonphoto/ecc_predictor.pt +3 -0
  44. link/amazonphoto/model_graph.pt +3 -0
  45. link/citeseer/CiteSeer.db +3 -0
  46. link/citeseer/ecc_predictor.pt +3 -0
  47. link/citeseer/model_graph.pt +3 -0
  48. link/coauthorcs/CoauthorCS.db +3 -0
  49. link/coauthorcs/ecc_predictor.pt +3 -0
  50. link/coauthorcs/model_graph.pt +3 -0
.gitattributes CHANGED
@@ -58,3 +58,36 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ graph/tu_cox2/TU_COX2.db filter=lfs diff=lfs merge=lfs -text
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+ graph/tu_dd/TU_DD.db filter=lfs diff=lfs merge=lfs -text
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+ link/cora/Cora.db filter=lfs diff=lfs merge=lfs -text
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+ link/cornell/Cornell.db filter=lfs diff=lfs merge=lfs -text
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+ link/dblp/DBLP.db filter=lfs diff=lfs merge=lfs -text
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+ link/pubmed/PubMed.db filter=lfs diff=lfs merge=lfs -text
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+ link/texas/Texas.db filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - graph-machine-learning
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+ pretty_name: M-DESIGN Knowledge Base and Model Artifacts
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+ ---
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+
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+ # M-DESIGN Knowledge Base and Model Artifacts
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+
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+ This dataset contains the released SQLite model-performance databases and model
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+ artifacts used by M-DESIGN, the method from "Beyond Model Base Retrieval:
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+ Weaving Knowledge to Master Fine-grained Neural Network Design".
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+
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+ ## Contents
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+
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+ Each `.db` file has a `model_records` table. The first six columns encode
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+ fine-grained neural design choices and the final two columns store the measured
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+ score and standard deviation. The `.pt` files archive the released PyTorch
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+ artifacts for the corresponding task and dataset.
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+
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+ Task layout:
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+
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+ - `node/`: node classification model records.
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+ - `link/`: link prediction model records.
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+ - `graph/`: graph classification model records.
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+
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+ ## Suggested Use
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+
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+ Place the downloaded folder at `data/knowledge_base` inside the M-DESIGN code
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+ repository, then run:
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+
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+ ```bash
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+ mdesign inspect-kb --task node_classification
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+ mdesign recommend --dataset Cora --task node_classification --max-iter 30
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+ ```
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+
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+ ## Notes
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+
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+ The databases are released for reproducibility and research use. Baseline logs,
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+ temporary trials, ablation outputs, TensorBoard events, private keys, and local
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+ machine paths are not part of this release bundle.
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