1.3k citations · 3.7k across the 55 of their papers we have counts for
13 papers · 1 filter
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…