# 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)}")
```