Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Authors: Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on several synthetic and real dynamic networks. The experimental results demonstrate that our proposed method significantly outperforms the existing methods by reducing 27% to 42% in terms of the maximum error for dynamic network reconstruction when fixing the number of restarts. |
| Researcher Affiliation | Academia | Ziwei Zhang,1 Peng Cui,1 Jian Pei,2 Xiao Wang,1 Wenwu Zhu1 1 Department of Computer Science and Technology, Tsinghua University, China 2 School of Computing Science, Simon Fraser University, Canada |
| Pseudocode | Yes | Algorithm 1 TIMERS: Theoretically Instructed Maximum Error-bounded Restart of SVD |
| Open Source Code | Yes | 1The code is available at http://nrl.thumedia.org/ |
| Open Datasets | Yes | All networks are publicly available at http://snap.stanford.edu/ or http://konect.uni-koblenz.de/ |
| Dataset Splits | No | The paper describes dividing dynamic changes into 'time slices with an equal number of changes' and 'randomly hid[ing] 10% of the network' for link prediction evaluation, but it does not specify a traditional training/validation/test split for model development or hyperparameter tuning. |
| Hardware Specification | Yes | All experiments are conducted in a single PC with 2 i7-6700 CPU and 24GB memory in MATLAB language. |
| Software Dependencies | No | The paper states that experiments were conducted in "MATLAB language" but does not provide a specific version number for MATLAB or any other software dependencies with their versions. |
| Experiment Setup | Yes | Specifically, in our experiment, we set the similarity matrix to be the adjacency matrix for simplicity, and k to be 100 as commonly used. ... The other important parameter is the error threshold Θ. Qualitatively, larger Θ will tolerate more error and leads to fewer number of restarts. |