Datasets:
id
stringclasses 5
values | node_feat
listlengths 4
10
| edge_index
listlengths 3
16
| edge_attr
listlengths 3
16
| y
listlengths 1
1
| num_nodes
int64 5
10
| num_edges
int64 3
16
| domain
stringclasses 4
values | task_type
stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
L2-STR-P01-graph
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[
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| 5
|
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|
regression
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trading-strategy
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[
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|
trading
|
regression
|
porter-five-forces
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[
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[
3.5
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| 12
|
strategy
|
regression
|
L2-COMP-P01-graph
|
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[
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[
0.9400000000000001
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| 3
|
competitive
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regression
|
mckinsey-7s-implementation
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[
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[
1
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| 8
|
organizational
|
classification
|
CLAK Consulting Knowledge Graph Dataset
Graph-ML dataset for consulting knowledge representation learning.
Dataset Structure
Inspired by OGB (Open Graph Benchmark) format:
| Field | Type | Description |
|---|---|---|
| node_feat | list[list[int]] | Node features (type, one-hot) |
| edge_index | list[tuple[int,int]] | Edge pairs (source, target) |
| edge_attr | list[int] | Edge types |
| y | list[float] | Target labels |
| num_nodes | int | Node count |
| domain | str | Consulting domain |
Node Types
- 0: Framework (McKinsey 7S, Porter, etc.)
- 1: Capability (L3 AI/ML)
- 2: Process (L2)
- 3: Flow (L1)
- 4: Insight
- 5: Data Source
Edge Types
- 0: USES
- 1: PART_OF
- 2: REQUIRES
- 3: GENERATES
- 4: NEURAL_LINK
Tasks
- Graph Classification: Predict success of framework implementation
- Graph Regression: Predict quality scores
- Link Prediction: Predict capability-process connections
Usage
from datasets import load_dataset
dataset = load_dataset("Kraft102/clak-consulting-graph-ml")
Citation
@misc{clak2026graph, title={CLAK Consulting Knowledge Graph Dataset}, author={CLAK Consulting AI}, year={2026} }
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