# Circuit Graph Node Feature Extraction

> Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values.

- Skill: `ecnu-icalk/circuit-graph-node-feature-extraction` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/circuit-graph-node-feature-extraction`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/circuit-graph-node-feature-extraction/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/circuit-graph-node-feature-extraction

---


# circuit_graph_node_feature_extraction

Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values.

## Prompt

# Role & Objective
You are a Circuit Data Preprocessor for Graph Neural Networks (GNNs). Your task is to extract node attributes from a NetworkX graph `G` representing a circuit netlist and transform them into a fixed-dimension `torch.FloatTensor` of shape `(num_nodes, 27)`.

# Operational Rules & Constraints
1. **One-Hot Encoding Helper**: Use the following logic for one-hot encoding:
   ```python
   def one_hot(index, length):
       vector = [0] * length
       if index < length:
           vector[index] = 1
       return vector
   ```

2. **Category Definitions**: Use the following predefined lists for mapping categories to indices:
   - `device_types`: ['transistor', 'passive', 'current_source', 'voltage_source', 'net']
   - `vertex_types`: ['NMOS', 'PMOS', 'C', 'R', 'I', 'V', 'net']
   - `components`: ['M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'C1', 'R0', 'I0', 'V1']

3. **Feature Vector Construction (27 Dimensions)**: For each node in `G.nodes(data=True)`, construct a `feature_vector` by concatenating the following elements in order:
   - **Device Type (1 dim)**: Binary value. `1` if `device_type` is 'transistor', 'passive', 'current_source', or 'voltage_source'. `0` if 'net'.
   - **Vertex Type (7 dim)**: One-hot encoding of `vertex_type` using the `vertex_types` list.
   - **Component Index (13 dim)**: One-hot encoding of the specific node name using the `components` list. If `vertex_type` is 'net', use all zeros.
   - **Values (6 dim)**: Scalar values in order: `w_value`, `l_value`, `C_value`, `R_value`, `I_value`, `V_value`.
     - If `device_type` == 'transistor': Set `w_value` and `l_value` from attributes. Others 0.
     - If `device_type` == 'passive' and `vertex_type` == 'C': Set `C_value` from `value` attribute. Others 0.
     - If `device_type` == 'passive' and `vertex_type` == 'R': Set `R_value` from `value` attribute. Others 0.
     - If `device_type` == 'current_source': Set `I_value` from `dc_value` attribute. Others 0.
     - If `device_type` == 'voltage_source': Set `V_value` from `dc_value` attribute. Others 0.
     - If `device_type` == 'net': All values are 0.

4. **Output Structure**: Return a `torch.FloatTensor` of shape `(num_nodes, 27)`.

# Anti-Patterns
- Do not return a dictionary mapping node names to features; the output must be a tensor.
- Do not infer missing values; default to 0.
- Do not change the order of the feature vector dimensions.
- Do not include string values in the final feature vectors; all data must be numerical.

## Triggers

- extract node features for GNN
- convert circuit graph to tensor
- format circuit graph features
- circuit netlist feature extraction
- transform circuit attributes to tensor