Kristina Lerman

Statistical and Nonlinear Physics · 2004–2020

A researcher in the Science corpus: 32 papers; trusts 174 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 223 citations into it; #776 of 10,127 by trust here, #846 by citations
• Sociology and Political Science — 186 citations into it; #3,917 of 22,756 by trust here, #2,394 by citations
• Artificial Intelligence — 152 citations into it; #10,938 of 35,345 by trust here, #4,656 by citations
• AI Ethics, Fairness & Society — 90 citations into it; #285 of 494 by trust here, #352 by citations
• Safety Research — 89 citations into it
• Information Systems — 76 citations into it; #2,268 of 8,771 by trust here, #1,867 by citations
• Safety, Robustness & Interpretability — 71 citations into it
• Communication — 50 citations into it; #181 of 3,460 by trust here, #544 by citations

Whom Kristina Lerman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Mark Newman
2. Matthew J. Salganik
3. Duncan J. Watts
4. Tad Hogg
5. Jure Leskovec
6. Lada A. Adamic
7. Damon Centola
8. Fang Wu
9. Rumi Ghosh
10. Emilio Ferrara

Who trusts Kristina Lerman overall

1. Nazanin Alipourfard
2. Tad Hogg
3. Greg Ver Steeg
4. Peter G. Fennell
5. Gregory Stoddard
6. Roja Bandari
7. Stefan Stieglitz
8. Rumi Ghosh
9. Ninareh Mehrabi
10. Emilio Ferrara

Papers from Kristina Lerman's perspective

Ordered by how strongly the researchers Kristina Lerman trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Et al
10. Going deeper with convolutions

Their papers

• A Survey on Bias and Fairness in Machine Learning (2019) — 6,091 citations
• The DARPA Twitter Bot Challenge (2016) — 408 citations
• Distributed online localization in sensor networks using a moving target (2004) — 296 citations
• How Visibility and Divided Attention Constrain Social Contagion (2012) — 141 citations
• What Stops Social Epidemics? (2011) — 98 citations
• Leveraging Position Bias to Improve Peer Recommendation (2014) — 110 citations
• The Simple Rules of Social Contagion (2013) — 313 citations
• The "Majority Illusion" in Social Networks (2015) — 226 citations
• Resource allocation in the grid using reinforcement learning (2004) — 135 citations
• Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data (2018) — 22 citations
• Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018) — 36 citations
• Computational social scientist beware: Simpson’s paradox in behavioral data (2017) — 32 citations
• Social dynamics of Digg (2010) — 166 citations
• Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign (2018) — 351 citations
• Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016) — 75 citations
• Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior (2015) — 82 citations
• Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (2020) — 729 citations
• Characterizing the 2016 Russian IRA influence campaign (2018) — 116 citations
• Analysis of Dynamic Task Allocation in Multi-Robot Systems (2006) — 302 citations
• A Geometric Solution to Fair Representations (2020) — 37 citations