Hypergraph Analysis Toolbox

Hypergraph Analysis Toolbox (HAT) is a software for the analysis and visualization of hypergraphs and higher-order structures. HAT implements tensor-based methods for the analysis of higher-order observability/controllability, coupling, similarity measures, and other structural properties. HAT contains methods for the following hypergraph tasks:

1. Construction and Visualization

2. Graph Expansions and Numeric Representations

3. Structural Properties

4. Dynamics, Observability, and Controllability

5. Similarity Measures

This software was designed to analyze higher-order biological data, such as chromatin structures observed with Pore-C. The capabilities and use cases of HAT are outlined in this PLOS Computational Biology article.

Contributors and Related Papers

Joshua Pickard, Can Chen, Rahmy Salman, Cooper Stansbury, Sion Kim, Amit Surana, Anthony Bloch, and Indika Rajapakse

Software Paper

8. Joshua Pickard, Can Chen, Rahmy Salman, Cooper Stansbury, Sion Kim, Amit Surana, Anthony Bloch, and Indika Rajapakse. “HAT: Hypergraph analysis toolbox.” PLOS Computational Biology 19, no. 6 (2023): e1011190.

Methods Papers

7. Joshua Pickard, Anthony M. Bloch, and Indika Rajapakse. “Scalable Hypergraphs Algorithms for Observability of Gene Regulation.” In 2025 23rd European Control Conference (ECC) [PDF]

6. Joshua Pickard, Can Chen, Cooper Stansbury, Amit Surana, Anthony M. Bloch, and Indika Rajapakse. “Kronecker product of tensors and hypergraphs: structure and dynamics.” SIAM Journal on Matrix Analysis and Applications 45, no. 3 (2024): 1621-1642. [PDF]

5. Joshua Pickard, Cooper Stansbury, Amit Surana, Indika Rajapakse, and Anthony Bloch. “Geometric aspects of observability of hypergraphs.” IFAC-PapersOnLine 58, no. 6 (2024): 321-326. [PDF]

4. Joshua Pickard, Amit Surana, Anthony Bloch, and Indika Rajapakse. “Observability of hypergraphs.” In 2023 62nd IEEE Conference on Decision and Control (CDC), pp. 2445-2451. IEEE, 2023. [PDF]

3. Amit Surana, Can Chen, and Indika Rajapakse. “Hypergraph similarity measures.” IEEE Transactions on Network Science and Engineering 10, no. 2 (2022): 658-674. [PDF]

2. Can Chen, Amit Surana, Anthony M. Bloch, and Indika Rajapakse. “Controllability of hypergraphs.” IEEE Transactions on Network Science and Engineering 8, no. 2 (2021): 1646-1657. [PDF]

1. Can Chen, and Indika Rajapakse. “Tensor entropy for uniform hypergraphs.” IEEE Transactions on Network Science and Engineering 7, no. 4 (2020): 2889-2900. [PDF]

Bug Reporting

Please report all bugs or defects in HAT to this page.

Contents:

• Installation
• HAT Documentation
• Datasets
• References

Indices and tables

• Index

• Module Index

• Search Page