activity
20162022
most citedCombining Label Propagation and Simple Models Out-performs Graph Neural Networks

114 citations · 282 across the 15 of their papers we have counts for

collaborators

44 papers

cs.LG2021

Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components

Nate Veldt, Austin R. Benson, Jon Kleinberg

Minimizing a sum of simple submodular functions of limited support is a special case of general submodular function minimization that has seen numerous applications in machine lear…

cs.SI2021

fauci-email: a json digest of Anthony Fauci's released emails

Austin R. Benson, Nate Veldt, David F. Gleich

A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemi…

cs.SI20215 cited

Edge Proposal Sets for Link Prediction

Abhay Singh, Qian Huang, Sijia Linda Huang +4

Graphs are a common model for complex relational data such as social networks and protein interactions, and such data can evolve over time (e.g., new friendships) and be noisy (e.g…

cs.LG20212 cited

Graph Belief Propagation Networks

Junteng Jia, Cenk Baykal, Vamsi K. Potluru +1

With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem. Graph neural networks are…

cs.DS2021

The Generalized Mean Densest Subgraph Problem

Nate Veldt, Austin R. Benson, Jon Kleinberg

Finding dense subgraphs of a large graph is a standard problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applicatio…

cs.LG2021

Choice Set Confounding in Discrete Choice

Kiran Tomlinson, Johan Ugander, Austin R. Benson

Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alt…