Evgeny Burnaev, Dmitrii Gavrilev
We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| realfolkcode/graphdiffusionanomaly | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| draw_hyperparameters | Ran | realfolkcode/graphdiffusionanomaly/run_benchmark.py pointer only (licence: NONE) · get_code("5bf83b3f8adb15fe") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of scorebased models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.15324")
get_code_for_paper("2306.15324")
have("2306.15324")
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