Favorite References for Hypergraph Data Science


Phil Chodrow


December 20, 2022

This bibliography is a selection of some of the papers that influence (or express) my ways of thinking about the challenges and opportunities in hypergraph network data science. It is extremely partial and includes an immodest emphasis on my own work.

I tend to cite or refer to papers on this list quite frequently.

The Importance of Higher-Order Structure

What are higher-order networks?
Christian Bick, Elizabeth Gross, Heather Harrington, and Michael T. Schaub
arXiv: 2104.11329 (2022)
A review of the landscape of polyadic data structures, including hypergraphs, emphasizing a mathematical perspective.

The why, how, and when of representations for complex systems
Leo Torres, Ann S. Blevins, Danielle Bassett, and Tina Eliassi-Rad
SIAM Review (2021)
A conceptual exploration of modeling considerations for complex interconnected systems.

Networks beyond pairwise interactions: Structure and dynamics
Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania Jean-Gabriel Young, and Giovanni Petri
Physics Reports (2020)
A review of the landscape of polyadic data structures, including hypergraphs, emphasizing a physics perspective.

No matching items

Community Detection

Nonbacktracking spectral clustering of nonuniform hypergraphs
Phil Chodrow, Nicole Eikmeier, and Jamie Haddock
Forthcoming in the SIAM Journal on Mathematics of Data Science (2022)
Developing eigenvector algorithms that explore the fundamental limits of hypergraph community detection.

Hypergraph cuts with general splitting functions
Nate Veldt, Austin Benson, and Jon Kleinberg
SIAM Review (2022)
A study of the s-t cut problem in hypergraphs that helpfully illustrates the considerable flexibility associated with scoring edges in community detection and partitioning tasks.

Generative hypergraph clustering: from blockmodels to modularity
Phil Chodrow, Nate Veldt, and Austin Benson
Science Advances (2021)
A fast modularity heuristic for hypergraph community detection.

How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs
Anton Eriksson, Daniel Edler, Alexis Rojas, Manlio de Domenico, and Martin Rosvall
Nature Communications Physics (2021)
A nice illustration of the considerable modeling choices that go into designing hypergraph community detection methods.

No matching items

Centrality, Ranking, and Core-Periphery

Core-periphery detection in hypergraphs
Francesco Tudisco and Desmond J. Higham
arXiv: 2202.12769 (2022)
A convex optimization approach to computing core-periphery scores in general hypergraphs.

Three hypergraph eigenvector centralities
Austin Benson
SIAM Journal on Mathematics of Data Science (2019)
Tensor eigenvector approaches to centrality in uniform hypergraphs.

No matching items

Hypergraph Reconstruction

Hypergraph reconstruction from network data
Jean-Gabriel Young, Giovanni Petri, and Tiago Peixoto
Nature Communications Physics (2021)
An inferential approach to the hypergraph reconstruction problem.

Supervised hypergraph reconstruction
Yanbang Wang and Jon Kleinberg
arXiv: 2211.13343 (2021)
An optimization-based approach to the hypergraph reconstruction problem.

No matching items

Null Models

Configuration models of random hypergraphs
Phil Chodrow
Journal of Complex Networks (2020)
Two configuration models for null-hypothesis testing in hypergraphs, with implications for triadic closure, edge correlations, and other topics.

No matching items

General Network Data Science

Configuring Random Graph Models with Fixed Degree Sequences
Bailey Fosdick, Daniel Larremore, Joel Nishimura, and Johan Ugander
SIAM Review (2020)
A foundational paper for constructing null models for networks and their generalizations.

The ground truth about metadata and community detection in networks
Leto Peel, Dan Larremore, and Aaron Clauset
Science Advances (2017)
Required reading for anyone who works in community detection or other unsupervised tasks in networks.

No matching items