1.3k citations · 3.7k across the 55 of their papers we have counts for
67 papers
Hypergraph patterns and collaboration structure
Jonas L. Juul, Austin R. Benson, Jon Kleinberg
Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly forme…
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Rongzhe Wei, Haoteng Yin, Junteng Jia +2
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured dat…
Graph-Based Methods for Discrete Choice
Kiran Tomlinson, Austin R. Benson
Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands t…
Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data
Darren C. Pagan, Calvin R. Pash, Austin R. Benson +1
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, gra…
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…
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…
Capturing Tie Strength with Algebraic Topology
Arnab Sarker, Jean-Baptiste Seby, Austin R. Benson +1
The association between tie strength and social structure is a fundamental topic in the social sciences. We study this association by analyzing tie strength in higher-order network…
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…
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…
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…
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…
Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data
Austin R. Benson, David F. Gleich, Desmond J. Higham
Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes, multilinear and tensor algebra, and more, to study complex systems. These are by now well establi…
A nonlinear diffusion method for semi-supervised learning on hypergraphs
Francesco Tudisco, Konstantin Prokopchik, Austin R. Benson
Hypergraphs are a common model for multiway relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given la…
Combinatorial Characterizations and Impossibilities for Higher-order Homophily
Nate Veldt, Austin R. Benson, Jon Kleinberg
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 fundame…
Random Graphs with Prescribed -Core Sequences: A New Null Model for Network Analysis
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
In the analysis of large-scale network data, a fundamental operation is the comparison of observed phenomena to the predictions provided by null models: when we find an interesting…
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Junteng Jia, Austin R. Benson
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and graph neural networks --…
Generative hypergraph clustering: from blockmodels to modularity
Philip S. Chodrow, Nate Veldt, Austin R. Benson
Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems. A standard analysis task is to identify clusters of closely related or densely interconn…
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Qian Huang, Horace He, Abhay Singh +2
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and wh…
Over-parametrized neural networks as under-determined linear systems
Austin R. Benson, Anil Damle, Alex Townsend
We draw connections between simple neural networks and under-determined linear systems to comprehensively explore several interesting theoretical questions in the study of neural n…
Learning Interpretable Feature Context Effects in Discrete Choice
Kiran Tomlinson, Austin R. Benson
The outcomes of elections, product sales, and the structure of social connections are all determined by the choices individuals make when presented with a set of options, so unders…
Communication-efficient distributed eigenspace estimation
Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Distributed computing is a standard way to scale up machine learning and data science algorithms to process large amounts of data. In such settings, avoiding communication amongst…
Augmented Sparsifiers for Generalized Hypergraph Cuts with Applications to Decomposable Submodular Function Minimization
Austin R. Benson, Jon Kleinberg, Nate Veldt
In recent years, hypergraph generalizations of many graph cut problems have been introduced and analyzed as a way to better explore and understand complex systems and datasets char…
A simple bipartite graph projection model for clustering in networks
Austin R. Benson, Paul Liu, Hao Yin
Graph datasets are frequently constructed by a projection of a bipartite graph, where two nodes are connected in the projection if they share a common neighbor in the bipartite gra…
Nonlinear Higher-Order Label Spreading
Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik
Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph. While there are…