Nate Veldt

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Recent Coverage of our Science Advances article on Hypergraph Homophily

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In celebration of the 25th anniversary of Watts and Strogatz’s seminal paper on small world networks, Nature Reviews Physics recently published a collection of six Research Spotlight articles on advances in network analysis (see 25 years of small-world network theory).

One of those six spotlights is a summary of my paper recently published in Science Advances (joint with Jon Kleinberg and Austin Benson) on higher-order measures of homophily. You can read the spotlight here: Measuring Similarities Within Groups. The original paper is here: Combinatorial characterizations and impossibilities for higher-order homophily

Our research findings on hypergraph homophily were also previously covered in an article on Phys.org, and an article in the Communications of the ACM.

 

SIAM ACDA Early Career Prize

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I am honored to have been selected for the 2023 SIAM Activity Group on Applied and Computational Discrete Algorithms Early Career Prize. This prize is given every two years to an early career researcher for “distinguished contributions to the field in the six calendar years prior to the award year.”

I have been a member of the ACDA Activity Group since early 2020, and have greatly enjoyed all the opportunities I have had to attend ACDA events and interact with other members. I look forward to giving a talk on my research at the upcoming 2023 ACDA conference.

Article Published in Science Advances

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My recent work on measures of homophily in group settings (joint work with Austin Benson and Jon Kleinberg) has just been published in Science Advances. I gave a brief overview of this research in an interview for a recent article that was published in a Communications of the ACM article.

Paper: https://www.science.org/doi/full/10.1126/sciadv.abq3200

Data: https://zenodo.org/record/7086798#.Y9CGkOzMIq0

Code: https://github.com/nveldt/HypergraphHomophily

Abstract

Homophily is the seemingly ubiquitous tendency for people to connect and interact with other individuals who are similar to them. This is a well-documented principle and is fundamental for how society organizes. Although many social interactions occur in groups, homophily has traditionally been measured using a graph model, which only accounts for pairwise interactions involving two individuals. Here, we develop a framework using hypergraphs to quantify homophily from group interactions. This reveals natural patterns of group homophily that appear with gender in scientific collaboration and political affiliation in legislative bill cosponsorship and also reveals distinctive gender distributions in group photographs, all of which cannot be fully captured by pairwise measures. At the same time, we show that seemingly natural ways to define group homophily are combinatorially impossible. This reveals important pitfalls to avoid when defining and interpreting notions of group homophily, as higher-order homophily patterns are governed by combinatorial constraints that are independent of human behavior but are easily overlooked.