Inhomogeneous Hypergraph Clustering with Applications
Authors: Pan Li, Olgica Milenkovic
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive testing of inhomogeneous partitioning in applications such as hierarchical biological network studies, structure learning of rankings and subspace clustering |
| Researcher Affiliation | Academia | Pan Li Department ECE UIUC panli2@illinois.edu Olgica Milenkovic Department ECE UIUC milenkov@illinois.edu |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code for experiments can be found at https://github.com/lipan00123/InHclustering. |
| Open Datasets | Yes | Here, we analyzed the Irish House of Parliament election dataset (2002) [38]. |
| Dataset Splits | No | The paper mentions using a 'training set' and 'sampling m rankings', but does not provide specific percentages or counts for training, validation, or test splits, nor does it refer to predefined splits with sufficient detail. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes sampling strategies for data but does not provide specific experimental setup details such as hyperparameter values, training configurations, or model-specific settings. |