Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Algorithms are further developed and the value of quasi-clustering analysis is illustrated with a study of internal migration within United States. As an example, we cluster a network that contains information about the internal migration between states of the United States for the year 2011 (Section 4). The quasi-clustering output unveils that migration is dominated by geographical proximity. |
| Researcher Affiliation | Academia | Gunnar Carlsson GUNNAR@MATH.STANFORD.EDU Department of Mathematics, Stanford University Facundo M emoli MEMOLI@MATH.OSU.EDU Department of Mathematics and Department of Computer Science and Engineering, Ohio State University Alejandro Ribeiro, Santiago Segarra {ARIBEIRO, SSEGARRA}@SEAS.UPENN.EDU Department of Electrical and Systems Engineering, University of Pennsylvania |
| Pseudocode | No | No structured pseudocode or algorithm block (e.g., labeled "Algorithm" or "Pseudocode") was found. The algorithm is described mathematically and in prose in Section 3.6. |
| Open Source Code | No | No explicit statement about releasing code or a link to a source code repository for the methodology described in the paper was found. |
| Open Datasets | Yes | The number of migrants from state to state in the U.S. is published yearly (United States Census Bureau, 2011). United States Census Bureau. State-to-state migration flows. U.S. Department of Commerce, 2011. URL http://www.census.gov/hhes/migration/ data/acs/state-to-state.html. |
| Dataset Splits | No | The paper uses a real-world dataset (US internal migration) but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software names with version numbers (e.g., programming languages, libraries, or solvers) used to implement or run the experiments were specified in the paper. |
| Experiment Setup | No | The paper applies a clustering method to a dataset, but it does not specify experimental setup details such as hyperparameters, learning rates, batch sizes, or model initialization settings, which are typically associated with machine learning experiments. |