Learning Scale-Free Networks by Dynamic Node Specific Degree Prior
Authors: Qingming Tang, Siqi Sun, Jinbo Xu
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l1 norm. |
| Researcher Affiliation | Academia | Qingming Tang1 QMTANG@TTIC.EDU Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, Illinois 60637, USA |
| Pseudocode | Yes | Algorithm 1 Update of node ranking; Algorithm 2 Edge rank updating |
| Open Source Code | No | No statement providing concrete access to source code or a link to a repository. |
| Open Datasets | Yes | Here we use the DREAM5 Network Inference Challenge dataset 1, which is a simulated gene expression data with 806 samples. DREAM5 also provides a ground truth network for this dataset. See (Marbach et al., 2012) for more details. ... To further test our method, we used DREAM5 dataset 3 and 4 respectively. ... See (Marbach et al., 2012) for a detailed description of the two data sets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and testing. It states the total number of samples for synthetic and real datasets, but not how they are partitioned. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions various methods (e.g., Glasso, RW, Hub) and algorithms (ADMM) but does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific library versions). |
| Experiment Setup | Yes | Our method uses 2 hyper-parameters: γ and β. Meanwhile, γ is the hyper parameter for the power-law distribution and λ controls sparsity. ... Hence we use γ = 2.5 in the following experiments. ... In our test, we use λ3 = 0.01 to yield the best performance. Besides,we set λ = λ1 = λ2 to produce a graph with a desired level of sparsity. |