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..
Neural Capacitated Clustering
Authors: Jonas K. Falkner, Lars Schmidt-Thieme
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on artificial data and two real world datasets our approach outperforms several state-of-the-art mathematical and heuristic solvers from the literature. |
| Researcher Affiliation | Academia | Jonas K. Falkner and Lars Schmidt-Thieme Institute of Computer Science, University of Hildesheim, Hildesheim, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: Capacitated k-means; Algorithm 2: Neural Capacitated Clustering (NCC) |
| Open Source Code | Yes | We open source our code at https://github.com/jokofa/NCC |
| Open Datasets | Yes | As real world datasets for capacitated spatial clustering we select the well-known Shanghai Telecom (ST) dataset [Wang et al., 2019]... The second dataset we assemble by matching the internet access sessions in the call record data of the Telecom Italia Milan (TIM) dataset [Barlacchi et al., 2015] with the Milan cell-tower grid retrieved from Open Celli D [OCID, 2021]... To evaluate our algorithm we choose the benchmark dataset of [Uchoa et al., 2017] |
| Dataset Splits | No | The paper mentions tuning parameters on a 'separate validation set' and discusses 'validation accuracy', but does not provide specific details on the size, percentage, or methodology of this validation split from the dataset. |
| Hardware Specification | Yes | All experiments are run on an i7-7700K CPU (4.20GHz). |
| Software Dependencies | Yes | We implement our model and the simple baselines in PyTorch [Paszke et al., 2019] version 1.11 and use Gurobi version 9.1.2 for all methods that require it. |
| Experiment Setup | Yes | We use L = 4 GNN layers, an embedding dimension of demb = 256 and a dimension of dh = 256 for all hidden layers... Then we do supervised training using binary cross entropy (BCE)... The fraction α of final nodes for which the re-prioritization is applied we treat as a hyperparameter... we set reasonable total run times of 3min for n = 200 and 15min for the original sizes. |