Overlap-Robust Decision Boundary Learning for Within-Network Classification
Authors: Sharad Nandanwar, M. N. Murty
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 sharadnandanwar@csa.iisc.ernet.in M. N. Murty Indian Institute of Science Bangalore,India mnm@csa.iisc.ernet.in |
| 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. |