Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
Authors: Ming Jin, Yuan-Fang Li, Shirui Pan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method demonstrates overwhelming superiority under both transductive and inductive settings on six real-world datasets |
| Researcher Affiliation | Academia | Ming Jin Monash University ming.jin@monash.edu Yuan-Fang Li Monash University yuanfang.li@monash.edu Shirui Pan Griffith University s.pan@griffith.edu.au |
| Pseudocode | Yes | Algorithm 1 Sampling Temporal Walks |
| Open Source Code | Yes | Code is available at https://github.com/KimMeen/Neural-Temporal-Walks |
| Open Datasets | Yes | We evaluate model performance on six real-world datasets. College Msg [17] is a social network dataset... Enron [17] is an email communication network. Taobao [46] is an attributed user behavior dataset... MOOC [17] is an attributed network... Wikipedia and Reddit [15] are two bipartite interaction networks... |
| Dataset Splits | Yes | In transductive link prediction, we sort and divide all N interactions in a dataset by time into three separate sets for training, validation, and testing. Specifically, the ranges of training, validation, and testing sets are [0, Ntrn), [Ntrn, Nval), [Nval, N], where Ntrn/N and Nval/N are 0.7 and 0.85. |
| Hardware Specification | No | The paper states, 'Some computing resources for this project are supported by MASSIVE 2.' and provides a URL to the MASSIVE website. However, it does not specify any exact GPU models, CPU models, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and the 'Runge-Kutta method' for solving ODEs, but it does not specify version numbers for any programming languages, libraries, or key software components required for replication. |
| Experiment Setup | Yes | Training Details. We implement and train all models under a unified evaluation framework with the Adam optimizer. The tuning of primary hyperparameters is discussed in Appendix C.3. In solving ODEs, we use the Runge-Kutta method with the number of function evaluations set to 8 by default. For fair comparisons and simplicity, we use sum-pooling when calculating node representations in both our method and CAWs. We also test Neur TWs , which is equipped with the binary anonymization, while Neur TWs adopts the default unitary anonymization. All methods are tuned thoroughly with nonlinear 2-layer and 3-layer perceptrons to conduct downstream link prediction and node classification tasks, and we adopt the commonly used Area Under the ROC Curve (AUC) and Average Precision (AP) as the evaluation metrics. |