Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
Authors: Katherine Tieu, Dongqi Fu, Yada Zhu, Hendrik Hamann, Jingrui He
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to the theoretical analysis, we also perform extensive experiments, not only demonstrating the superiority of Temp-G3NTK in the temporal graph classification task, but also showing that Temp-G3NTK can achieve very competitive performance in node-level tasks like node classification compared with various SOTA graph kernel and representation learning baselines. |
| Researcher Affiliation | Collaboration | Katherine Tieu University of Illinois Urbana-Champaign kt42@illinois.edu; Dongqi Fu Meta AI dongqifu@meta.com; Yada Zhu IBM Research yzhu@us.ibm.com; Hendrik Hamann IBM Research hendrikh@us.ibm.com; Jingrui He University of Illinois Urbana-Champaign jingrui@illinois.edu |
| Pseudocode | Yes | The pseudo-code of computing the Temp-G3NTK kernel as above is shown in Appendix A. |
| Open Source Code | Yes | Our code is available at https://github.com/kthrn22/ Temp GNTK |
| Open Datasets | Yes | Targeting temporal graph classification, we conduct experiments on one of the most advanced temporal graph benchmarks that have graph-level labels, i.e., TUDataset 5 [32], the four datasets are INFECTIOUS, DBLP, FACEBOOK, and TUMBLR, the detailed dataset statistics can also be found in Appendix G.1. Additionally, we also leveraged the more large-scale temporal datasets REDDIT, WIKIPEDIA, LASTFM, and MOOC from [25]6. |
| Dataset Splits | Yes | For each dataset above, we evaluate the temporal graph classification accuracy by conducting 5-fold cross-validation and then report the mean and standard deviation of test accuracy. To be specific, given a dataset of n temporal graphs {G1, G2, ..., Gn} and their labels {y1, y2, ..., yn}... The training, validation, and test sets of tgbn-trade are defined in the TGB package with 70%/15%/15% chronological splits. |
| Hardware Specification | No | The paper provides runtime comparisons in Table 3 but does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | We adopt the implementations of Graph Kernels from GRAKEL library [43] and the implementations Graph Representation Learning methods from the Karate Club library [40]. We adopt the default hyperparameters from implementations of both libraries. |
| Experiment Setup | Yes | upon obtaining the time representation as in Eq. 1, we let the dimension of the time representation be dt = 25 and α = β = dt. In order to leverage Temp-G3NTK for graph classification, we employ C-SVM as a kernel regression predictor with the gram matrix of pairwise Temp-G3NTK values of the training set as the pre-computed kernel. The regularization parameter C of the SVM classifier is sampled evenly from 120 values in the interval [10 2, 104], in log scale, and set the number of maximum iterations to 5 105. For the number of BLOCK operations in our Temp-G3NTK formula, L, we search for L over {1, 2, 3}. |