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..
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). |