| A Review and Framework for Modeling Complex Engineered System Development Processes | 0 | 0.34 | 2022 |
| Which contributions count? Analysis of attribution in open source | 2 | 0.39 | 2021 |
| Accurate inference of crowdsourcing properties when using efficient allocation strategies | 0 | 0.34 | 2019 |
| UAFS: Uncertainty-Aware Feature Selection for Problems with Missing Data. | 0 | 0.34 | 2019 |
| Neural language representations predict outcomes of scientific research. | 0 | 0.34 | 2018 |
| An information-theoretic, all-scales approach to comparing networks. | 2 | 0.37 | 2018 |
| Crowd ideation of supervised learning problems. | 0 | 0.34 | 2018 |
| Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution | 1 | 0.35 | 2018 |
| Crowdsourcing Predictors of Residential Electric Energy Usage. | 2 | 0.37 | 2018 |
| Inferring the size of the causal universe: features and fusion of causal attribution networks. | 0 | 0.34 | 2018 |
| The quoter model: a paradigmatic model of the social flow of written information. | 1 | 0.35 | 2017 |
| Which friends are more popular than you? Contact strength and the friendship paradox in social networks. | 1 | 0.35 | 2017 |
| Information flow reveals prediction limits in online social activity. | 4 | 0.40 | 2017 |
| Predicting an Effect Event from a New Cause Event Using a Semantic Web Based Abstraction Tree of Past Cause-Effect Event Pairs. | 0 | 0.34 | 2017 |
| Autocompletion interfaces make crowd workers slower, but their use promotes response diversity. | 0 | 0.34 | 2017 |
| Reply & Supply: Efficient crowdsourced exploration for growing question sets and nets. | 0 | 0.34 | 2016 |
| What we write about when we write about causality: Features of causal statements across large-scale social discourse. | 0 | 0.34 | 2016 |
| Selection models of language production support informed text partitioning: an intuitive and practical, bag-of-phrases framework for text analysis. | 0 | 0.34 | 2016 |
| Reply To Garcia Et Al.: Common Mistakes In Measuring Frequency-Dependent Word Characteristics | 1 | 0.35 | 2015 |
| Identifying missing dictionary entries with frequency-conserving context models. | 4 | 0.56 | 2015 |
| Zipf'S Law Holds For Phrases, Not Words | 1 | 0.39 | 2014 |
| Understanding the group dynamics and success of teams. | 6 | 0.56 | 2014 |
| Constructing a taxonomy of fine-grained human movement and activity motifs through social media. | 0 | 0.34 | 2014 |
| Human Language Reveals A Universal Positivity Bias | 34 | 1.86 | 2014 |
| Quantifying Information Flow During Emergencies | 15 | 0.75 | 2014 |
| Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language. | 1 | 0.49 | 2014 |
| Shadow networks: Discovering hidden nodes with models of information flow. | 3 | 0.45 | 2013 |
| Robustness of skeletons and salient features in networks. | 2 | 0.41 | 2013 |
| Natural emergence of clusters and bursts in network evolution | 4 | 0.45 | 2012 |
| \"Quantifying Bias in Social and Mainstream Media\" by Yu-Ru Lin, James P. Bagrow, and David Lazer with Ching-man Au Yeung as coordinator | 1 | 0.36 | 2012 |
| Spatiotemporal features of human mobility | 0 | 0.34 | 2012 |
| Communities And Bottlenecks: Trees And Treelike Networks Have High Modularity | 0 | 0.34 | 2012 |
| The role of caretakers in disease dynamics | 0 | 0.34 | 2012 |
| More Voices Than Ever? Quantifying Media Bias in Networks | 17 | 1.29 | 2011 |
| Collective Response Of Human Populations To Large-Scale Emergencies | 93 | 4.93 | 2011 |
| Flavor Network And The Principles Of Food Pairing | 76 | 4.81 | 2011 |
| Robustness And Modular Structure In Networks | 5 | 0.47 | 2011 |
| Investigating Bimodal Clustering in Human Mobility | 5 | 0.48 | 2009 |
