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a4f86c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Iterative PageRank Model
Non-autoregressive iterative transformer that learns PageRank via shared-weight refinement.
## Architecture
- **Params:** 796,032
- **Layers:** 4 (shared across 8 iterations)
- **Width:** 128, **Heads:** 4
- **Graph size:** 64 nodes (directed)
- **Damping:** 0.85
## Task
Given a directed graph's adjacency matrix, predict each node's PageRank value.
The model learns power iteration implicitly through iterative refinement.
## Metrics
- **KL divergence:** between predicted and true PR distribution
- **Ranking accuracy:** pairwise ordering correctness
- **Top-k accuracy:** overlap of predicted vs true top-k important nodes
## Usage
```python
import torch
from iterative_pagerank import IterativePageRankModel, PageRankConfig, generate_batch
ckpt = torch.load("model.pt", weights_only=True)
config = PageRankConfig(**ckpt["config"])
model = IterativePageRankModel(config)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
adj, targets, meta = generate_batch(1, config, edge_prob=0.1,
graph_weights=(0.0, 0.0, 1.0), device="cpu")
with torch.no_grad():
all_prs = model(adj, n_iters=64)
print(f"Predicted: {all_prs[-1][0].topk(5)}")
print(f"True: {targets[0].topk(5)}")
```
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