HomeRun Graph and Hypergraph Null Model Replication Data

Replication data for the HomeRun algorithm, which performs curveball trades in streaming for fast null modeling of graphs, hypergraphs, and binary matrices. The dataset was authored by Matteo Riondato and last updated in January 2026. Specific details on the data volume and structure are unavailable.

Use Cases

• Implement and benchmark the HomeRun algorithm for generating null models of graphs and hypergraphs.
• Validate the performance of curveball trade operations in a streaming context for binary matrices.
• Reproduce the computational experiments from the associated research paper on fast null modeling.

Strengths

• Data is directly associated with a published research algorithm (HomeRun).
• Maintained by a known author (Matteo Riondato) and updated recently in 2026.

Limitations

• The dataset description and structure are not detailed, as only a README file is referenced.
• Core data metrics like row count, column count, and file formats are unknown.
• The scope and size of the provided replication data are unspecified.

Provenance

Source

Matteo Riondato (data mammoths) Dataverse

Collection Method

Generated as replication data for the HomeRun research algorithm.

Freshness

Last updated on 2026-01-20.