We lifted 12 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| qiyan98/swingnn | canonical | 3 of 9 |
| dsl-lab/swingnn | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| window_partition | Ran | qiyan98/swingnn/model/swin_gnn/swin_gnn.py code served (permissive licence) · get_code("144d10b49baeb8a6") |
| citeseer_ego | Ran | dsl-lab/swingnn/setup/gen_graph_data.py code served (permissive licence) · get_code("33e65ffda4746402") |
| weight_init | Ran | qiyan98/swingnn/model/unet/unet_edm.py code served (permissive licence) · get_code("d41a4250066bce93") |
| window_reverse | Ran | qiyan98/swingnn/model/swin_gnn/swin_gnn.py code served (permissive licence) · get_code("61bf152e6a42a184") |
| clustering_worker | Not yet run | qiyan98/swingnn/evaluation/stats.py code served (permissive licence) · get_code("85b7ab6da324d0ed") |
| compute_score | Not yet run | qiyan98/swingnn/runner/sanity_check_helper.py code served (permissive licence) · get_code("632d673f64dd5dcc") |
| construct_atomic_number_array | Not yet run | qiyan98/swingnn/setup/mol_preprocess.py code served (permissive licence) · get_code("0c1e757e3c9f7161") |
| degree_worker | Not yet run | qiyan98/swingnn/evaluation/stats.py code served (permissive licence) · get_code("36afd1c572ae4673") |
| gen_graph_comm_grid_lobster | Not yet run | dsl-lab/swingnn/setup/gen_graph_data.py code served (permissive licence) · get_code("eefc727475cbfb4d") |
| get_all_permutation | Not yet run | qiyan98/swingnn/runner/sanity_check_helper.py code served (permissive licence) · get_code("c20bf9ed5347eaa1") |
| n_community | Not yet run | dsl-lab/swingnn/setup/gen_graph_data.py code served (permissive licence) · get_code("d520133b61f52af3") |
| pad_array | Not yet run | qiyan98/swingnn/evaluation/mmd.py code served (permissive licence) · get_code("d1af971a6d642b82") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing network and utilizes shifted window based self-attention inspired by SwinTransformers. Further, through systematic ablations, we identify several critical training and sampling techniques that significantly improve the sample quality of graph generation. At last, we introduce a simple post-processing trick, $\textit{i.e.}$, randomly permuting the generated graphs, which provably converts any graph generative model to a permutation-invariant one. Extensive experiments on synthetic and real-world protein and molecule datasets show that our SwinGNN achieves state-of-the-art performances. Our code is released at https://github.com/qiyan98/SwinGNN.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.01646")
get_code_for_paper("2307.01646")
have("2307.01646")
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