1.3k citations · 3.7k across the 55 of their papers we have counts for
16 papers · 1 filter
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…
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Derek Lim, Austin R. Benson
Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are…
Hypergraph Clustering for Finding Diverse and Experienced Groups
Ilya Amburg, Nate Veldt, Austin R. Benson
When forming a team or group of individuals, we often seek a balance of expertise in a particular task while at the same time maintaining diversity of skills within each group. Her…
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
There is inherent information captured in the order in which we write words in a list. The orderings of binomials --- lists of two words separated by `and' or `or' --- has been stu…
Better Set Representations For Relational Reasoning
Qian Huang, Horace He, Abhay Singh +3
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set o…
Minimizing Localized Ratio Cut Objectives in Hypergraphs
Nate Veldt, Austin R. Benson, Jon Kleinberg
Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data. G…
Entrywise convergence of iterative methods for eigenproblems
Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Several problems in machine learning, statistics, and other fields rely on computing eigenvectors. For large scale problems, the computation of these eigenvectors is typically perf…
Residual Correlation in Graph Neural Network Regression
Junteng Jia, Austin R. Benson
A graph neural network transforms features in each vertex's neighborhood into a vector representation of the vertex. Afterward, each vertex's representation is used independently f…
Choice Set Optimization Under Discrete Choice Models of Group Decisions
Kiran Tomlinson, Austin R. Benson
The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually h…
Hypergraph Cuts with General Splitting Functions
Nate Veldt, Austin R. Benson, Jon Kleinberg
The minimum - cut problem in graphs is one of the most fundamental problems in combinatorial optimization, and graph cuts underlie algorithms throughout discrete mathematics,…