Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions
Authors: Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Toyotaro Suzumura, Manish Singh
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on eight standard datasets show that our method significantly outperforms related methods on various tasks for dynamic graphs. |
| Researcher Affiliation | Collaboration | 1 Indian Institute of Technology Hyderabad, India 2 RWTH Aachen, Germany 3 Zerotha Research and Cerence Gmbh, Germany 4 The University of Tokyo, Japan |
| Pseudocode | Yes | 1: function fct = ESpectral(At, H(l) t , W (l) t 1) 2: W (l) t = RNN(W (l) t 1) 3: H(l+1) t = GNN(At, H(l) t , W (l) t ) 4: vgt = Pool(At, H(l+1) t ) 5: fct = W2 σ (W1vgt) 6: end function |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We borrow datasets and its preprocessing/splitting settings used in previous best baselines (Xiang, Huang, and Wang 2022; Pareja et al. 2020). Datasets: Table ?? summarizes eight datasets for link prediction, edge classification, and node classification. Each dataset contains a sequence of time-ordered graphs. ...Finally, for Brain dataset, nodes represent tiny regions/cubes in the brain, and the edges are their connectivity. (Xu et al. 2019b) |
| Dataset Splits | Yes | Datasets: Table ?? summarizes eight datasets for link prediction, edge classification, and node classification. Each dataset contains a sequence of time-ordered graphs. ... # Time Steps Task (Train / Val / Test) BC-OTC 95 / 14 / 28 EC |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | No | The best hyperparameters search has the range as: Number of layers {1, 2}, Hidden dimension {32, 64, 128}, Number of heads {4, 8, 16}, Filter order {4, 8, 16}, Wavelet scales [0.1, 10]. The rest of the parameters and settings are borrowed from previous works (Pareja et al. 2020; Xiang, Huang, and Wang 2022). |