Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Link Prediction with Spatial and Temporal Consistency in Dynamic Networks
Authors: Wenchao Yu, Wei Cheng, Charu C Aggarwal, Haifeng Chen, Wei Wang
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,466 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,016 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |