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].
SnapNETS: Automatic Segmentation of Network Sequences with Node Labels
Authors: Sorour Amiri, Liangzhe Chen, B. Prakash
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several diverse real datasets show that it ๏ฌnds cut points matching ground-truth or meaningful external signals outperforming non-trivial baselines. |
| Researcher Affiliation | Academia | Sorour E. Amiri, Liangzhe Chen, B. Aditya Prakash Department of Computer Science, Virginia Tech Email: EMAIL |
| Pseudocode | Yes | Alg. 1 shows the ๏ฌnal pseudo code of SNAPNETS. Algorithm 2: LAYERED-ALP |
| Open Source Code | Yes | Our experiments were conducted on a 4 Xeon E7-4850 CPU with 512GB of 1066Mhz main memory and our code is available for research purposes3. 3http://github.com/Sorour Amiri/Snap NETS |
| Open Datasets | Yes | Datasets. We collected a number of datasets from various domains such as social and news media, epidemiology, autonomous system, and co-authorship network to evaluate SNAPNETS. See Tab. 3 for a summary description. ... 4http://topology.eecs.umich.edu/data.html 5http://ndssl.vbi.vt.edu/synthetic-data/ |
| Dataset Splits | No | No explicit information on dataset splits (e.g., training, validation, test percentages or counts) is provided. The paper mentions using 'datasets' and 'ground truth'. |
| Hardware Specification | Yes | Our experiments were conducted on a 4 Xeon E7-4850 CPU with 512GB of 1066Mhz main memory |
| Software Dependencies | No | The paper states, 'We implemented SNAPNETS in MATLAB and Python,' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We use the same amount of coarsening (ฯ = 0.1) as in (Purohit et al. 2014). We extracted multiple standard features (Li et al. 2012) and eliminate correlated ones to get eight features for each Gc i (See Tab. 2 for a description). Finally, we normalize them by range normalization for a meaningful comparison between the features (Li et al. 2012). |