| # Iterative PageRank Model |
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| Non-autoregressive iterative transformer that learns PageRank via shared-weight refinement. |
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|
| ## 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. |
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| ## 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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