We lifted 10 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| yu-rp/dualprism | canonical | 6 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| add_noise | Ran | yu-rp/dualprism/src/aug.py pointer only (licence: NONE) · get_code("ae4f1bded5dc4c34") |
| generate_threshold_vector | Ran | yu-rp/dualprism/src/aug.py pointer only (licence: NONE) · get_code("a61699b46d3d13d8") |
| graph_numpy2tensor | Ran | yu-rp/dualprism/src/utils.py pointer only (licence: NONE) · get_code("0b1f01b346d86095") |
| prepare_dataset | Ran | yu-rp/dualprism/src/utils.py pointer only (licence: NONE) · get_code("cbf4d0892481c707") |
| prepare_synthetic_dataset | Ran | yu-rp/dualprism/src/utils.py pointer only (licence: NONE) · get_code("7a73d187862ecb75") |
| triplet_loss | Ran | yu-rp/dualprism/DIG/dig/auggraph/method/DualPrism/utils/utils.py pointer only (licence: NONE) · get_code("c6e56bc4d0f6bab6") |
| approximate_hamming_similarity | Not yet run | yu-rp/dualprism/DIG/dig/auggraph/method/DualPrism/utils/utils.py pointer only (licence: NONE) · get_code("95afc6f0962b5c2a") |
| euclidean_distance | Not yet run | yu-rp/dualprism/DIG/dig/auggraph/method/DualPrism/utils/utils.py pointer only (licence: NONE) · get_code("69e7dcdc10127913") |
| spectral_noise | Not yet run | yu-rp/dualprism/src/aug.py pointer only (licence: NONE) · get_code("983c6560bfc8086d") |
| spectral_noise | Not yet run | yu-rp/dualprism/DIG/dig/auggraph/method/DualPrism/utils/augmentation.py pointer only (licence: NONE) · get_code("34cc38b600742cf0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graph Neural Networks have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and restricted structural changes persist. This leads to the question: Is it possible to develop more property-conserving and structure-sensitive augmentation methods? Through a spectral lens, we investigate the interplay between graph properties, their augmentation, and their spectral behavior, and observe that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs. These observations inform our introduction of the Dual-Prism (DP) augmentation methods, including DP-Noise and DP-Mask, which retain essential graph properties while diversifying augmented graphs. Extensive experiments validate the efficiency of our approach, providing a new and promising direction for graph data augmentation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.09953")
get_code_for_paper("2401.09953")
have("2401.09953")
Connect an agent — have() is free.