TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Authors: Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |