Inferring the structure and scale of modular networks. - Jake M. Hofman; Chris H. Wiggins 001 (MLG@ICML, 2008)

Characterizing individual communication patterns. - R. Dean Malmgren; Jake M. Hofman; Luís A. Nunes Amaral; Duncan J. Watts (KDD, 2009)

Inferring relevant social networks from interpersonal communication. - Munmun De Choudhury; Winter A. Mason; Jake M. Hofman; Duncan J. Watts (WWW, 2010)

Graphical models for inferring single molecule dynamics. - Jonathan E. Bronson; Jake M. Hofman; Jingyi Fei; Ruben L. Gonzalez; Chris H. Wiggins 001 (BMC Bioinform., 2010)

Predicting consumer behavior with Web search. - Sharad Goel; Jake M. Hofman; Sébastien Lahaie; David M. Pennock; Duncan J. Watts (Proc. Natl. Acad. Sci. USA, 2010)

Everyone's an influencer: quantifying influence on twitter. - Eytan Bakshy; Jake M. Hofman; Winter A. Mason; Duncan J. Watts (WSDM, 2011)

Who says what to whom on twitter. - Shaomei Wu; Jake M. Hofman; Winter A. Mason; Duncan J. Watts (WWW, 2011)

Who Does What on the Web: A Large-Scale Study of Browsing Behavior. - Sharad Goel; Jake M. Hofman; M. Irmak Sirer (ICWSM, 2012)

Sharding social networks. - Quang Duong 004; Sharad Goel; Jake M. Hofman; Sergei Vassilvitskii (WSDM, 2013)

Scalable Recommendation with Poisson Factorization. - Prem Gopalan; Jake M. Hofman; David M. Blei (CoRR, 2013)

A large-scale exploration of group viewing patterns. - Allison June-Barlow Chaney; Mike Gartrell; Jake M. Hofman; John Guiver; Noam Koenigstein; Pushmeet Kohli; Ulrich Paquet (TVX, 2014)

Estimating the Causal Impact of Recommendation Systems from Observational Data. - Amit Sharma 007; Jake M. Hofman; Duncan J. Watts (EC, 2015)

Scalable Recommendation with Hierarchical Poisson Factorization. - Prem Gopalan; Jake M. Hofman; David M. Blei (UAI, 2015)

Improving Comprehension of Numbers in the News. - Pablo Javier Barrio; Daniel G. Goldstein; Jake M. Hofman (CHI, 2016)

Exploring Limits to Prediction in Complex Social Systems. - Travis Martin; Jake M. Hofman; Amit Sharma 007; Ashton Anderson; Duncan J. Watts (WWW, 2016)

Split-door criterion for causal identification: Automatic search for natural experiments. - Amit Sharma 007; Jake M. Hofman; Duncan J. Watts (CoRR, 2016)

The Structural Virality of Online Diffusion. - Sharad Goel; Ashton Anderson; Jake M. Hofman; Duncan J. Watts (Manag. Sci., 2016)

What's Happening and What Happened: Searching the Social Web. - Omar Alonso; Vasileios Kandylas; Serge-Eric Tremblay; Jake M. Hofman; Siddhartha Sen 001 (WebSci, 2017)

To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand. - Christopher Riederer; Jake M. Hofman; Daniel G. Goldstein (CHI, 2018)

How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results. - Jake M. Hofman; Daniel G. Goldstein; Jessica Hullman (CHI, 2020)

Expanding the Scope of Reproducibility Research Through Data Analysis Replications. - Jake M. Hofman; Daniel G. Goldstein; Siddhartha Sen 001; Forough Poursabzi-Sangdeh (WWW (Companion Volume), 2020)

How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt. - Gabriel Kaptchuk; Daniel G. Goldstein; Eszter Hargittai; Jake M. Hofman; Elissa M. Redmiles (CoRR, 2020)

Manipulating and Measuring Model Interpretability. - Forough Poursabzi-Sangdeh; Daniel G. Goldstein; Jake M. Hofman; Jennifer Wortman Vaughan; Hanna M. Wallach (CHI, 2021)

Datamations: Animated Explanations of Data Analysis Pipelines. - Xiaoying Pu; Sean Kross; Jake M. Hofman; Daniel G. Goldstein (CHI, 2021)

Putting scientific results in perspective: Improving the communication of standardized effect sizes. - Yea-Seul Kim; Jake M. Hofman; Daniel G. Goldstein (CHI, 2022)

Round Numbers Can Sharpen Cognition. - Huy Anh Nguyen; Jake M. Hofman; Daniel G. Goldstein (CHI, 2022)

Investigating Perceptual Biases in Icon Arrays. - Cindy Xiong; Ali Sarvghad; Daniel G. Goldstein; Jake M. Hofman; Çagatay Demiralp (CHI, 2022)

How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids. - Gabriel Kaptchuk; Daniel G. Goldstein; Eszter Hargittai; Jake M. Hofman; Elissa M. Redmiles (DTRAP, 2022)

Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. - Jamelle Watson-Daniels; Solon Barocas; Jake M. Hofman; Alexandra Chouldechova (FAccT, 2023)

Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment. - Sofia Eleni Spatharioti; David M. Rothschild; Daniel G. Goldstein; Jake M. Hofman (CoRR, 2023)

Comparing scalable strategies for generating numerical perspectives. - Hancheng Cao; Sofia Eleni Spatharioti; Daniel G. Goldstein; Jake M. Hofman (CoRR, 2023)

REFORMS: Reporting Standards for Machine Learning Based Science. - Sayash Kapoor; Emily Cantrell; Kenny Peng; Thanh Hien Pham; Christopher A. Bail; Odd Erik Gundersen; Jake M. Hofman; Jessica Hullman; Michael A. Lones; Momin M. Malik; Priyanka Nanayakkara; Russell A. Poldrack; et al. (CoRR, 2023)

Pre-registration for Predictive Modeling. - Jake M. Hofman; Angelos Chatzimparmpas; Amit Sharma 007; Duncan J. Watts; Jessica Hullman (CoRR, 2023)

Using Open Data to Automatically Generate Localized Analogies. - Sofia Eleni Spatharioti; Daniel G. Goldstein; Jake M. Hofman (CHI, 2024)

ChatBench: From Static Benchmarks to Human-AI Evaluation. - Serina Chang; Ashton Anderson; Jake M. Hofman (ACL (1), 2025)

Math Education With Large Language Models: Peril or Promise? - Harsh Kumar; David M. Rothschild; Daniel G. Goldstein; Jake M. Hofman (AIED (4), 2025)

Effects of LLM-based Search on Decision Making: Speed, Accuracy, and Overreliance. - Sofia Eleni Spatharioti; David M. Rothschild; Daniel G. Goldstein; Jake M. Hofman (CHI, 2025)

The Agentic Economy. - David M. Rothschild; Markus Mobius; Jake M. Hofman; Eleanor Wiske Dillon; Daniel G. Goldstein; Nicole Immorlica; Sonia Jaffe; Brendan Lucier; Aleksandrs Slivkins; Matthew Vogel (CoRR, 2025)

Bringing Everyone to the Table: An Experimental Study of LLM-Facilitated Group Decision Making. - Mohammed Alsobay; David M. Rothschild; Jake M. Hofman; Daniel G. Goldstein (CoRR, 2025)