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 [1].
A Generative Model for Dynamic Networks with Applications
Authors: Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati7842-7849
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches. |
| Researcher Affiliation | Academia | Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati Department of Computer Science and Automation Indian Institute of Science Bangalore 56012, India email: EMAIL |
| Pseudocode | Yes | Algorithm 1 Generating Synthetic Networks |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | 1) Enron email: The Enron dataset (Klimt and Yang 2004)... 2) NIPS co-authorship: There are 17 snapshots in this network... (Heaukulani and Ghahramani 2013). 3) Infocom: This dataset contains information... (Kim and Leskovec 2013). |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits, such as percentages, sample counts, or explicit validation set usage. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions general software components like LSTM, k-means, and spectral clustering, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper provides parameters for synthetic data generation (e.g., n, T, K, s1, s2, s3, s4) and some choices for evaluation (k-means range), but it does not specify concrete experimental setup details such as learning rates, batch sizes, optimizers, or number of epochs for model training. |