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 | [
[
2,
1,
0,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
0,
0,
0,
1,
0
],
[
0,
0,
0,
1,
0
],
[
4,
0,
0,
0,
1
]
] | [
[
0,
1
],
[
0,
2
],
[
0,
3
],
[
0,
4
],
[
0,
6
]
] | [
0,
0,
0,
0,
0
] | [
0.9
] | 7 | 5 | strategy | regression |
trading-strategy | [
[
3,
1,
0,
0,
0
],
[
2,
0,
1,
0,
0
],
[
2,
0,
1,
0,
0
],
[
2,
0,
1,
0,
0
],
[
2,
0,
1,
0,
0
],
[
1,
0,
0,
1,
0
],
[
1,
0,
0,
1,
0
],
[
1,
0,... | [
[
0,
1
],
[
0,
2
],
[
0,
3
],
[
0,
4
],
[
1,
5
],
[
2,
6
],
[
3,
7
],
[
4,
5
],
[
8,
1
],
[
8,
2
],
[
8,
3
],
[
8,
4
],
[
1,
9
],
[
2,
9
],
[
3,... | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | [
1.25
] | 10 | 16 | trading | regression |
porter-five-forces | [
[
0,
1,
0,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
2,
0,
0,
1,
0
],
[
5,
0,... | [
[
0,
1
],
[
0,
2
],
[
0,
3
],
[
0,
4
],
[
0,
5
],
[
6,
1
],
[
6,
2
],
[
6,
3
],
[
6,
4
],
[
6,
5
],
[
7,
6
],
[
6,
8
]
] | [
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
3
] | [
3.5
] | 9 | 12 | strategy | regression |
L2-COMP-P01-graph | [
[
2,
1,
0,
0,
0
],
[
1,
0,
1,
0,
0
],
[
0,
0,
0,
1,
0
],
[
4,
0,
0,
0,
1
]
] | [
[
0,
1
],
[
0,
2
],
[
0,
4
]
] | [
0,
0,
0
] | [
0.9400000000000001
] | 5 | 3 | competitive | regression |
mckinsey-7s-implementation | [
[
0,
1,
0,
0,
0
],
[
1,
0,
1,
0,
0
],
[
1,
0,
1,
0,
0
],
[
2,
0,
0,
1,
0
],
[
2,
0,
0,
1,
0
],
[
4,
0,
0,
0,
1
],
[
5,
0,
0,
0,
1
]
] | [
[
0,
1
],
[
0,
2
],
[
3,
1
],
[
4,
2
],
[
3,
5
],
[
4,
5
],
[
6,
3
],
[
6,
4
]
] | [
2,
2,
0,
0,
3,
3,
1,
1
] | [
1
] | 7 | 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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