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..
Overlap-Robust Decision Boundary Learning for Within-Network Classification
Authors: Sharad Nandanwar, M. N. Murty
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive comparative study on different real-world datasets, we found that our method improves over the state-of-the-art approaches. |
| Researcher Affiliation | Academia | Sharad Nandanwar Indian Institute of Science Bangalore,India EMAIL M. N. Murty Indian Institute of Science Bangalore,India EMAIL |
| Pseudocode | Yes | Algorithm 1 Training Re SLMin using SGD. |
| Open Source Code | Yes | We make our code and datasets publicly available at https://github.com/sharadnandanwar/Re SLMin. |
| Open Datasets | Yes | We make our code and datasets publicly available at https://github.com/sharadnandanwar/Re SLMin. Datasets used: Pub Med, Co RA, Amazon, Wikipedia. Table 1 summarizes some of the statistics of datasets used. For studying the robustness of the proposed approach with increase in class overlap, we synthesize a network of 10000 nodes using LFR benchmark (Lancichinetti, Fortunato, and Radicchi 2008). |
| Dataset Splits | Yes | For each experiment, we perform 20 runs using different realizations of the train and test sets, having 10% and 90% of nodes respectively. To determine the optimal values of regularization parameter, we perform 10-fold cross-validation with the given set of training nodes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | To determine the optimal values of regularization parameter, we perform 10-fold cross-validation with the given set of training nodes. The parameter values are chosen using grid search technique where λ and μ both were in the range {2 5, 2 6, . . . , 2 15}. Also, we set the value of parameter α, responsible for neighborhood effect, as 0.5. |