Link Prediction with Spatial and Temporal Consistency in Dynamic Networks
Authors: Wenchao Yu, Wei Cheng, Charu C Aggarwal, Haifeng Chen, Wei Wang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of California Los Angeles 2NEC Laboratories America, Inc. 3IBM T.J. Watson Research Center |
| Pseudocode | Yes | Algorithm 1: Algorithm for LIST model |
| Open Source Code | No | The paper does not explicitly state that the source code for the LIST model is made publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | To verify the performance of the LIST model, we conduct experiments on four dynamic networks, namely Infectious [Isella et al., 2011], UCI Msg [Opsahl and Panzarasa, 2009], Digg1 and DBLP2, as shown in Table 1. ... 1http://konect.uni-koblenz.de/networks 2http://dblp.uni-trier.de/xml |
| Dataset Splits | No | The paper describes using a training set (T-omega to T-1) and a test set (Tth timestamp), but does not explicitly mention a separate validation set or split for hyperparameter tuning, nor does it describe how hyperparameters were determined using a validation process. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions) used for the experiments. |
| Experiment Setup | Yes | The other parameter settings are as follows: iteration number B = 100 for the computation of P(t), latent dimension k = 20, exponential decay θ = 0.3, sliding window size ω = 5, propagation balancing weight λ = 0.3, regularizer weights βi = 0.01. The maximum number of iterations of the LIST model is set to 200. |