MERIT: Learning Multi-level Representations on Temporal Graphs
Authors: Binbin Hu, Zhengwei Wu, Jun Zhou, Ziqi Liu, Zhigang Huangfu, Zhiqiang Zhang, Chaochao Chen
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four public datasets demonstrate the effectiveness of MERIT on both (inductive/transductive) link prediction and node classification task. |
| Researcher Affiliation | Collaboration | Binbin Hu1 , Zhengwei Wu1 , Jun Zhou1 , Ziqi Liu 1 , Zhigang Huangfu1 , Zhiqiang Zhang1 and Chaochao Chen2 1Ant Group 2Zhejiang University {bin.hbb, zejun.wzw, ziqiliu, zhigang.hfzg, lingyao.zzq, jun.zhoujun}@antfin.com zjuccc@zju.edu.cn |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to its source code, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | We conduct extensive experiments on four widely used datasets [Kumar et al., 2019] from different domains, namly Reddit, Wikipedia, MOOC and Last FM. |
| Dataset Splits | No | The paper mentions evaluating on datasets in 'transductive' and 'inductive' settings for link prediction and node classification, but does not explicitly provide specific percentages, sample counts, or citations to predefined splits for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as libraries or solvers. |
| Experiment Setup | Yes | The paper details hyperparameter variations for its ablation studies, such as the number of periodic kernels (k in Eq. 5) varied in {1, 3, 5, 7, 9} and the truncated dimension (d in Eq. 3) varied in {8, 16, 32, 64, 128, 256}. It states that 'the optimal performance is achieved with k = 5' and 'MERIT achieves the best performance when d = 32 or d = 64'. |