1.3k citations · 3.7k across the 55 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
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…
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 --…
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…
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…
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…
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…
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…
Graph-based Semi-Supervised & Active Learning for Edge Flows
Junteng Jia, Michael T. Schaub, Santiago Segarra +1
We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to pr…
Neural Jump Stochastic Differential Equations
Junteng Jia, Austin R. Benson
Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not hav…
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
Austin R. Benson, Jason D. Lee, Bartek Rajwa +1
Numerous algorithms are used for nonnegative matrix factorization under the assumption that the matrix is nearly separable. In this paper, we show how to make these algorithms effi…