Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Authors: Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, Pan Li
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 15% AUC gain in the inductive setting. CAW-N also outperforms previous methods in 5 out of the 6 networks in the transductive setting. 5 EXPERIMENTS |
| Researcher Affiliation | Academia | Yanbang Wang1 , Yen-Yu Chang2, Yunyu Liu3, Jure Leskovec1, Pan Li1,3 1Department of Computer Science, 2Electrical Engineering, Stanford University 3Department of Computer Science, Purdue University |
| Pseudocode | Yes | Algorithm 1: Temporal Walk Extraction (E, α, M, m, w0, t0), Algorithm 2: Online probability computation (G, α), Algorithm 3: Iterative Sampling (E, α, wp, tp) |
| Open Source Code | Yes | Project website with code and data: http://snap.stanford.edu/caw/ Their code is provided in the supplement. |
| Open Datasets | Yes | We use six real-world public datasets: Wikipedia is a network between wiki pages and human editors. Reddit is a network between posts and users on subreddits. MOOC is a network of students and online course content units. Social Evolution is a network recording the physical proximity between students. Enron is a email communication network. UCI is a network between online posts made by students. We summarize their statistics in Tab.1 and give their detailed description and access in Appendix C.1. (Appendix C.1 provides specific URLs for each dataset). |
| Dataset Splits | Yes | In our implementation, we split the total time range [0, T] into three intervals: [0, Ttrain), [Ttrain, Tval), [Tval, T]. links occurring within each interval are dedicated to training, validation, and testing set, respectively. For all datasets, we fix Ttrain/T=0.7, and Tval/T=0.85. |
| Hardware Specification | Yes | All the experiments were carried out on a Ubuntu 16.04 server with Xeon Gold 6148 2.4 GHz 40-core CPU, Nvidia 2080 Ti RTX 11GB GPU, and 768 GB memory. |
| Software Dependencies | No | The paper mentions the operating system (Ubuntu 16.04) but does not list specific software dependencies (e.g., libraries, frameworks, or solvers) with version numbers. |
| Experiment Setup | Yes | on all datasets, we train both variants with mini-batch size 32 and set learning rate = 1.0 10 4; the maximum training epoch number is 50 though in practice we observe that with early stopping we usually find the optimal epoch in fewer than 10 epochs; our early stopping strategy is that if the validation performance does not increase for more than 3 epoch then we stop and use the third previous epoch for testing; dropout layers with dropout probability = 0.1 are added to the RNN module, the MLP modules, and the self-attention pooling layer. Table 4: Hyperparameter search range of CAW sampling. |