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10/05/2022
Hypergraph patterns and collaboration structure
Humans collaborate in different contexts such as in creative or scientif...
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07/22/2022
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Graph neural networks (GNNs) have shown superiority in many prediction t...
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05/23/2022
Graph-Based Methods for Discrete Choice
Choices made by individuals have widespread impacts–for instance, people...
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10/28/2021
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Minimizing a sum of simple submodular functions of limited support is a ...
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06/30/2021
Edge Proposal Sets for Link Prediction
Graphs are a common model for complex relational data such as social net...
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06/06/2021
Graph Belief Propagation Networks
With the wide-spread availability of complex relational data, semi-super...
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06/02/2021
The Generalized Mean Densest Subgraph Problem
Finding dense subgraphs of a large graph is a standard problem in graph ...
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05/17/2021
Choice Set Confounding in Discrete Choice
Standard methods in preference learning involve estimating the parameter...
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03/27/2021
A nonlinear diffusion method for semi-supervised learning on hypergraphs
Hypergraphs are a common model for multiway relationships in data, and h...
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03/22/2021
Higher-order Homophily is Combinatorially Impossible
Homophily is the seemingly ubiquitous tendency for people to connect wit...
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02/24/2021
Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis
In the analysis of large-scale network data, a fundamental operation is ...
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01/24/2021
Generative hypergraph clustering: from blockmodels to modularity
Hypergraphs are a natural modeling paradigm for a wide range of complex ...
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01/19/2021
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Semi-supervised learning on graphs is a widely applicable problem in net...
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10/29/2020
Over-parametrized neural networks as under-determined linear systems
We draw connections between simple neural networks and under-determined ...
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10/27/2020
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Graph Neural Networks (GNNs) are the predominant technique for learning ...
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09/07/2020
Learning Interpretable Feature Context Effects in Discrete Choice
The outcomes of elections, product sales, and the structure of social co...
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09/05/2020
Communication-efficient distributed eigenspace estimation
Distributed computing is a standard way to scale up machine learning and...
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07/16/2020
Augmented Sparsifiers for Generalized Hypergraph Cuts
In recent years, hypergraph generalizations of many graph cut problems h...
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07/01/2020
A simple bipartite graph projection model for clustering in networks
Graph datasets are frequently constructed by a projection of a bipartite...
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06/15/2020
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Many platforms collect crowdsourced information primarily from volunteer...
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06/10/2020
Fair Clustering for Diverse and Experienced Groups
The ability for machine learning to exacerbate bias has led to many algo...
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06/08/2020
Nonlinear Higher-Order Label Spreading
Label spreading is a general technique for semi-supervised learning with...
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03/07/2020
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text
There is inherent information captured in the order in which we write wo...
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02/21/2020
Localized Flow-Based Clustering in Hypergraphs
Local graph clustering algorithms are designed to efficiently detect sma...
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02/19/2020
Entrywise convergence of iterative methods for eigenproblems
Several problems in machine learning, statistics, and other fields rely ...
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02/02/2020
Choice Set Optimization Under Discrete Choice Models of Group Decisions
The way that people make choices or exhibit preferences can be strongly ...
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01/09/2020
Hypergraph Cuts with General Splitting Functions
The minimum s-t cut problem in graphs is one of the most fundamental pro...
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10/22/2019
Hypergraph clustering with categorical edge labels
Graphs and networks are a standard model for describing data or systems ...
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10/01/2019
Retrieving Top Weighted Triangles in Graphs
Pattern counting in graphs is a fundamental primitive for many network a...
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09/03/2019
Incrementally Updated Spectral Embeddings
Several fundamental tasks in data science rely on computing an extremal ...
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05/24/2019
Neural Jump Stochastic Differential Equations
Many time series can be effectively modeled with a combination of contin...
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05/17/2019
Graph-based Semi-Supervised & Active Learning for Edge Flows
We present a graph-based semi-supervised learning (SSL) method for learn...
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05/14/2019
Planted Hitting Set Recovery in Hypergraphs
In various application areas, networked data is collected by measuring i...
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02/06/2019
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Large Question-and-Answer (Q&A) platforms support diverse knowledge cura...
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11/28/2018
Core-fringe link prediction
Data collection often involves the partial measurement of a larger syste...
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07/25/2018
Three hypergraph eigenvector centralities
Eigenvector centrality is a standard network analysis tool for determini...
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07/13/2018
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian
Modeling complex systems and data with graphs has been a mainstay of the...
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05/03/2018
Found Graph Data and Planted Vertex Covers
A typical way in which network data is recorded is to measure all the in...
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05/02/2018
Computing tensor Z-eigenvectors with dynamical systems
We present a new framework for computing Z-eigenvectors of general tenso...
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02/20/2018
Simplicial Closure and Higher-order Link Prediction
Networks provide a powerful formalism for modeling complex systems, by r...
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02/19/2018
Tools for higher-order network analysis
Networks are a fundamental model of complex systems throughout the scien...
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04/12/2017
Higher-order clustering in networks
A fundamental property of complex networks is the tendency for edges to ...
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12/29/2016
Motifs in Temporal Networks
Networks are a fundamental tool for modeling complex systems in a variet...
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02/27/2014
