Research

Some topics I’m working on (or have worked on recently) include:

  • Applications of configuration-type random graph models in data science.
  • Adaptive and evolving networks.
  • Dynamic hierarchies in evolving networks.
  • Community detection and network inference.
  • Information-theoretic methods in spatial analysis.

Some of my recent collaborators are:

Papers

Moments of uniform random multigraphs with fixed degree sequences

Phil Chodrow (2020). SIMODS.
What’s the expected adjacency matrix of a uniformly random multigraph with fixed degree sequence?

Configuration models of random hypergraphs

Phil Chodrow (2020). Journal of Complex Networks.
Random null models for principled hypothesis-testing in polyadic data.

Local symmetry and global structure in adaptive voter models

Phil Chodrow, Peter Mucha (2020). SIAM Journal on Applied Mathematics.
New approximations for opinion dynamics on coevolving graphs.

Annotated hypergraphs: models and applications

Phil Chodrow, Andrew Mellor (2020). Applied Network Science.
Sampling theory, metrics, and vignettes for studying higher-order networks with node-edge incidence annotations. Generalizes directed …

Structure and information in spatial segregation

Phil Chodrow (2017). Proceedings of the National Academy of Sciences.
Information geometry and machine learning for studying spatial segregation.

Demand and congestion in multiplex transportation networks

Data-informed modeling and optimization of multimodal transportation systems.

Upper and lower bounds for the iterates of order-preserving homogeneous maps on cones

Phil Chodrow, Cole Franks, Brian Lins (2012). Linear Algebra.
Novel bounds for a class of dynamical systems on the nonnegative cone.

Preprints

Hypergraph clustering: from blockmodels to modularity

Phil Chodrow, Nate Veldt, Austin Benson (2021). arXiv:2101.09611.
A statistically-motivated extension of network modularity to hypergraphs.

Emergence of hierarchy in networked endorsement dynamics

A mathematical model of hierarchy in networks, which we study analytically and statistically.

Log-minor distributions and an application to estimating mean subsystem entropy

Alice Schwarze, Phil Chodrow, Mason Porter (2018). arXiv:1901.09456.
Mathematics of estimating mean subsystem entropy in jointly Gaussian dynamical systems.

Divergence, entropy, information: an opinionated introduction to information theory

Phil Chodrow (2017). arXiv: 1708.07459.
An invitation to information theory for complex systems scientists and other nonspecialists.