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2020 – today
- 2024
[j17]Kiran Tomlinson
, Austin R. Benson:
Graph-based methods for discrete choice. Netw. Sci. 12(1): 21-40 (2024)- 2023
- [j16]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Augmented Sparsifiers for Generalized Hypergraph Cuts. J. Mach. Learn. Res. 24: 207:1-207:50 (2023) - 2022
- [j15]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Hypergraph Cuts with General Splitting Functions. SIAM Rev. 64(3): 650-685 (2022) - [j14]Junteng Jia, Austin R. Benson:
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations. SIAM J. Math. Data Sci. 4(1): 100-125 (2022) - [c43]Konstantin Prokopchik, Austin R. Benson, Francesco Tudisco:
Nonlinear Feature Diffusion on Hypergraphs. ICML 2022: 17945-17958 - [c42]Austin R. Benson, Nate Veldt, David F. Gleich:
Fauci-Email: A JSON Digest of Anthony Fauci's Released Emails. ICWSM 2022: 1208-1217 - [c41]Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li:
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. NeurIPS 2022 - [c40]Ilya Amburg, Nate Veldt, Austin R. Benson:
Diverse and Experienced Group Discovery via Hypergraph Clustering. SDM 2022: 145-153 - [d1]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Combinatorial Measures of Higher-order Homophily for Group Interaction Datasets. Zenodo, 2022
- [i66]Kiran Tomlinson, Austin R. Benson:
Graph-Based Methods for Discrete Choice. CoRR abs/2205.11365 (2022) - [i65]Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li:
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. CoRR abs/2207.11311 (2022) - [i64]Jonas L. Juul, Austin R. Benson, Jon M. Kleinberg:
Hypergraph patterns and collaboration structure. CoRR abs/2210.02163 (2022) - 2021
- [j13]Xiang Fu, Shangdi Yu, Austin R. Benson:
Modelling and analysis of tagging networks in Stack Exchange communities. J. Complex Networks 8(5) (2021) - [j12]Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Communication-Efficient Distributed Eigenspace Estimation. SIAM J. Math. Data Sci. 3(4): 1067-1092 (2021) - [c39]Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson:
Combining Label Propagation and Simple Models out-performs Graph Neural Networks. ICLR 2021 - [c38]Derek Lim, Austin R. Benson:
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. ICWSM 2021: 373-384 - [c37]Kiran Tomlinson, Johan Ugander, Austin R. Benson:
Choice Set Confounding in Discrete Choice. KDD 2021: 1571-1581 - [c36]Kiran Tomlinson, Austin R. Benson:
Learning Interpretable Feature Context Effects in Discrete Choice. KDD 2021: 1582-1592 - [c35]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
The Generalized Mean Densest Subgraph Problem. KDD 2021: 1604-1614 - [c34]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. NeurIPS 2021: 3744-3756 - [c33]Katherine Van Koevering, Austin R. Benson, Jon M. Kleinberg:
Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis. WWW 2021: 367-378 - [c32]Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik:
Nonlinear Higher-Order Label Spreading. WWW 2021: 2402-2413 - [i63]Junteng Jia, Austin R. Benson:
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations. CoRR abs/2101.07730 (2021) - [i62]Philip S. Chodrow, Nate Veldt, Austin R. Benson:
Generative hypergraph clustering: from blockmodels to modularity. CoRR abs/2101.09611 (2021) - [i61]Katherine Van Koevering, Austin R. Benson, Jon M. Kleinberg:
Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis. CoRR abs/2102.12604 (2021) - [i60]Austin R. Benson, David F. Gleich, Desmond J. Higham:
Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data. CoRR abs/2103.05031 (2021) - [i59]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Higher-order Homophily is Combinatorially Impossible. CoRR abs/2103.11818 (2021) - [i58]Francesco Tudisco, Konstantin Prokopchik, Austin R. Benson:
A nonlinear diffusion method for semi-supervised learning on hypergraphs. CoRR abs/2103.14867 (2021) - [i57]Kiran Tomlinson, Johan Ugander, Austin R. Benson:
Choice Set Confounding in Discrete Choice. CoRR abs/2105.07959 (2021) - [i56]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
The Generalized Mean Densest Subgraph Problem. CoRR abs/2106.00909 (2021) - [i55]Junteng Jia, Cenk Baykal, Vamsi K. Potluru, Austin R. Benson:
Graph Belief Propagation Networks. CoRR abs/2106.03033 (2021) - [i54]Abhay Singh, Qian Huang, Sijia Linda Huang, Omkar Bhalerao, Horace He, Ser-Nam Lim, Austin R. Benson:
Edge Proposal Sets for Link Prediction. CoRR abs/2106.15810 (2021) - [i53]Austin R. Benson, Nate Veldt, David F. Gleich:
fauci-email: a json digest of Anthony Fauci's released emails. CoRR abs/2108.01239 (2021) - [i52]Arnab Sarker, Jean-Baptiste Seby, Austin R. Benson, Ali Jadbabaie:
Higher Order Information Identifies Tie Strength. CoRR abs/2108.02091 (2021) - [i51]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. CoRR abs/2110.14859 (2021) - 2020
- [j11]Hao Yin, Austin R. Benson, Johan Ugander:
Measuring directed triadic closure with closure coefficients. Netw. Sci. 8(4): 551-573 (2020) - [j10]Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie:
Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian. SIAM Rev. 62(2): 353-391 (2020) - [j9]Thomas Reeves, Anil Damle, Austin R. Benson:
Network Interpolation. SIAM J. Math. Data Sci. 2(2): 505-528 (2020) - [j8]Huda Nassar, Austin R. Benson, David F. Gleich:
Neighborhood and PageRank methods for pairwise link prediction. Soc. Netw. Anal. Min. 10(1): 63 (2020) - [c31]Kiran Tomlinson, Austin R. Benson:
Choice Set Optimization Under Discrete Choice Models of Group Decisions. ICML 2020: 9514-9525 - [c30]Junteng Jia, Austin R. Benson:
Residual Correlation in Graph Neural Network Regression. KDD 2020: 588-598 - [c29]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Minimizing Localized Ratio Cut Objectives in Hypergraphs. KDD 2020: 1708-1718 - [c28]Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Entrywise convergence of iterative methods for eigenproblems. NeurIPS 2020 - [c27]Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson:
Better Set Representations For Relational Reasoning. NeurIPS 2020 - [c26]Raunak Kumar, Paul Liu, Moses Charikar, Austin R. Benson:
Retrieving Top Weighted Triangles in Graphs. WSDM 2020: 295-303 - [c25]Katherine Van Koevering, Austin R. Benson, Jon M. Kleinberg:
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text. WWW 2020: 606-616 - [c24]Ilya Amburg, Nate Veldt, Austin R. Benson:
Clustering in graphs and hypergraphs with categorical edge labels. WWW 2020: 706-717 - [c23]Huda Nassar, Caitlin Kennedy, Shweta Jain, Austin R. Benson, David F. Gleich:
Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations. WWW 2020: 2927-2933 - [i50]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Hypergraph Cuts with General Splitting Functions. CoRR abs/2001.02817 (2020) - [i49]Kiran Tomlinson, Austin R. Benson:
Choice Set Optimization Under Discrete Choice Models of Group Decisions. CoRR abs/2002.00421 (2020) - [i48]Junteng Jia, Austin R. Benson:
Outcome Correlation in Graph Neural Network Regression. CoRR abs/2002.08274 (2020) - [i47]Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Entrywise convergence of iterative methods for eigenproblems. CoRR abs/2002.08491 (2020) - [i46]Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Localized Flow-Based Clustering in Hypergraphs. CoRR abs/2002.09441 (2020) - [i45]Katherine Van Koevering, Austin R. Benson, Jon M. Kleinberg:
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text. CoRR abs/2003.03612 (2020) - [i44]Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson:
Set-Structured Latent Representations. CoRR abs/2003.04448 (2020) - [i43]Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik:
Nonlinear Higher-Order Label Spreading. CoRR abs/2006.04762 (2020) - [i42]Ilya Amburg, Nate Veldt, Austin R. Benson:
Fair Clustering for Diverse and Experienced Groups. CoRR abs/2006.05645 (2020) - [i41]Derek Lim, Austin R. Benson:
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. CoRR abs/2006.08108 (2020) - [i40]Austin R. Benson, Paul Liu, Hao Yin:
A simple bipartite graph projection model for clustering in networks. CoRR abs/2007.00761 (2020) - [i39]Austin R. Benson, Jon M. Kleinberg, Nate Veldt:
Augmented Sparsifiers for Generalized Hypergraph Cuts. CoRR abs/2007.08075 (2020) - [i38]Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Communication-efficient distributed eigenspace estimation. CoRR abs/2009.02436 (2020) - [i37]Kiran Tomlinson, Austin R. Benson:
Learning Interpretable Feature Context Effects in Discrete Choice. CoRR abs/2009.03417 (2020) - [i36]Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson:
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. CoRR abs/2010.13993 (2020) - [i35]Austin R. Benson, Anil Damle, Alex Townsend:
Over-parametrized neural networks as under-determined linear systems. CoRR abs/2010.15959 (2020)
2010 – 2019
- 2019
- [j7]Austin R. Benson, David F. Gleich:
Computing Tensor Z-Eigenvectors with Dynamical Systems. SIAM J. Matrix Anal. Appl. 40(4): 1311-1324 (2019) - [j6]Austin R. Benson:
Three Hypergraph Eigenvector Centralities. SIAM J. Math. Data Sci. 1(2): 293-312 (2019) - [c22]Huda Nassar, Austin R. Benson, David F. Gleich:
Pairwise link prediction. ASONAM 2019: 386-393 - [c21]Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson:
Graph-based Semi-Supervised & Active Learning for Edge Flows. KDD 2019: 761-771 - [c20]Kun Dong, Austin R. Benson, David Bindel:
Network Density of States. KDD 2019: 1152-1161 - [c19]Junteng Jia, Austin R. Benson:
Neural Jump Stochastic Differential Equations. NeurIPS 2019: 9843-9854 - [c18]Paul Liu, Austin R. Benson, Moses Charikar:
Sampling Methods for Counting Temporal Motifs. WSDM 2019: 294-302 - [c17]Hao Yin, Austin R. Benson, Jure Leskovec:
The Local Closure Coefficient: A New Perspective On Network Clustering. WSDM 2019: 303-311 - [c16]Junteng Jia, Austin R. Benson:
Random Spatial Network Models for Core-Periphery Structure. WSDM 2019: 366-374 - [c15]Austin R. Benson, Jon M. Kleinberg:
Link Prediction in Networks with Core-Fringe Data. WWW 2019: 94-104 - [c14]Jan Overgoor, Austin R. Benson, Johan Ugander:
Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice. WWW 2019: 1409-1420 - [i34]Xiang Fu, Shangdi Yu, Austin R. Benson:
Modeling and Analysis of Tagging Networks in Stack Exchange Communities. CoRR abs/1902.02372 (2019) - [i33]Thomas Reeves, Anil Damle, Austin R. Benson:
Network interpolation. CoRR abs/1905.01253 (2019) - [i32]Ilya Amburg, Jon M. Kleinberg, Austin R. Benson:
Planted Hitting Set Recovery in Hypergraphs. CoRR abs/1905.05839 (2019) - [i31]Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson:
Graph-based Semi-Supervised & Active Learning for Edge Flows. CoRR abs/1905.07451 (2019) - [i30]Kun Dong, Austin R. Benson, David Bindel:
Network Density of States. CoRR abs/1905.09758 (2019) - [i29]Junteng Jia, Austin R. Benson:
Neural Jump Stochastic Differential Equations. CoRR abs/1905.10403 (2019) - [i28]Hao Yin, Austin R. Benson, Johan Ugander:
Measuring Directed Triadic Closure with Closure Coefficients. CoRR abs/1905.10683 (2019) - [i27]Huda Nassar, Austin R. Benson, David F. Gleich:
Pairwise Link Prediction. CoRR abs/1907.04503 (2019) - [i26]Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Incrementally Updated Spectral Embeddings. CoRR abs/1909.01188 (2019) - [i25]Raunak Kumar, Paul Liu, Moses Charikar, Austin R. Benson:
Retrieving Top Weighted Triangles in Graphs. CoRR abs/1910.00692 (2019) - [i24]Ilya Amburg, Nate Veldt, Austin R. Benson:
Hypergraph clustering with categorical edge labels. CoRR abs/1910.09943 (2019) - 2018
- [j5]Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg:
Simplicial closure and higher-order link prediction. Proc. Natl. Acad. Sci. USA 115(48): E11221-E11230 (2018) - [c13]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
Sequences of Sets. KDD 2018: 1148-1157 - [c12]Austin R. Benson, Jon M. Kleinberg:
Found Graph Data and Planted Vertex Covers. NeurIPS 2018: 1363-1374 - [c11]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
A Discrete Choice Model for Subset Selection. WSDM 2018: 37-45 - [i23]Austin R. Benson:
Tools for higher-order network analysis. CoRR abs/1802.06820 (2018) - [i22]Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg:
Simplicial Closure and Higher-order Link Prediction. CoRR abs/1802.06916 (2018) - [i21]Austin R. Benson, David F. Gleich:
Computing tensor Z-eigenvectors with dynamical systems. CoRR abs/1805.00903 (2018) - [i20]Austin R. Benson, Jon M. Kleinberg:
Found Graph Data and Planted Vertex Covers. CoRR abs/1805.01209 (2018) - [i19]Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie:
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian. CoRR abs/1807.05044 (2018) - [i18]Austin R. Benson:
Three hypergraph eigenvector centralities. CoRR abs/1807.09644 (2018) - [i17]Junteng Jia, Austin R. Benson:
Detecting Core-Periphery Structure in Spatial Networks. CoRR abs/1808.06544 (2018) - [i16]Paul Liu, Austin R. Benson, Moses Charikar:
A sampling framework for counting temporal motifs. CoRR abs/1810.00980 (2018) - [i15]Jan Overgoor, Austin R. Benson, Johan Ugander:
Choosing to grow a graph: Modeling network formation as discrete choice. CoRR abs/1811.05008 (2018) - [i14]Austin R. Benson, Jon M. Kleinberg:
Core-fringe link prediction. CoRR abs/1811.11540 (2018) - 2017
- [j4]Austin R. Benson, David F. Gleich, Lek-Heng Lim:
The Spacey Random Walk: A Stochastic Process for Higher-Order Data. SIAM Rev. 59(2): 321-345 (2017) - [c10]Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich:
Local Higher-Order Graph Clustering. KDD 2017: 555-564 - [c9]Ashwin Paranjape, Austin R. Benson, Jure Leskovec:
Motifs in Temporal Networks. WSDM 2017: 601-610 - [i13]Hao Yin, Austin R. Benson, Jure Leskovec:
Higher-order clustering in networks. CoRR abs/1704.03913 (2017) - 2016
- [j3]Grey Ballard, Austin R. Benson, Alex Druinsky, Benjamin Lipshitz, Oded Schwartz:
Improving the Numerical Stability of Fast Matrix Multiplication. SIAM J. Matrix Anal. Appl. 37(4): 1382-1418 (2016) - [c8]Tao Wu, Austin R. Benson, David F. Gleich:
General Tensor Spectral Co-clustering for Higher-Order Data. NIPS 2016: 2559-2567 - [c7]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
Modeling User Consumption Sequences. WWW 2016: 519-529 - [c6]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
On the Relevance of Irrelevant Alternatives. WWW 2016: 963-973 - [i12]Austin R. Benson, David F. Gleich, Lek-Heng Lim:
The Spacey Random Walk: a Stochastic Process for Higher-order Data. CoRR abs/1602.02102 (2016) - [i11]Tao Wu, Austin R. Benson, David F. Gleich:
General Tensor Spectral Co-clustering for Higher-Order Data. CoRR abs/1603.00395 (2016) - [i10]Austin R. Benson, David F. Gleich, Jure Leskovec:
Higher-order organization of complex networks. CoRR abs/1612.08447 (2016) - [i9]Ashwin Paranjape, Austin R. Benson, Jure Leskovec:
Motifs in Temporal Networks. CoRR abs/1612.09259 (2016) - 2015
- [j2]Austin R. Benson, Sven Schmit, Robert Schreiber:
Silent error detection in numerical time-stepping schemes. Int. J. High Perform. Comput. Appl. 29(4): 403-421 (2015) - [c5]Austin R. Benson, Grey Ballard:
A framework for practical parallel fast matrix multiplication. PPoPP 2015: 42-53 - [c4]Austin R. Benson, David F. Gleich, Jure Leskovec:
Tensor Spectral Clustering for Partitioning Higher-order Network Structures. SDM 2015: 118-126 - [i8]Austin R. Benson, David F. Gleich, Jure Leskovec:
Tensor Spectral Clustering for Partitioning Higher-order Network Structures. CoRR abs/1502.05058 (2015) - [i7]Grey Ballard, Austin R. Benson, Alex Druinsky, Benjamin Lipshitz, Oded Schwartz:
Improving the numerical stability of fast matrix multiplication algorithms. CoRR abs/1507.00687 (2015) - 2014
- [j1]Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, Lexing Ying:
A Parallel Directional Fast Multipole Method. SIAM J. Sci. Comput. 36(4) (2014) - [c3]Austin R. Benson, Carlos Riquelme, Sven Schmit:
Learning multifractal structure in large networks. KDD 2014: 1326-1335 - [c2]Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich:
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices. NIPS 2014: 945-953 - [i6]Austin R. Benson, Carlos Riquelme, Sven Schmit:
Learning multifractal structure in large networks. CoRR abs/1402.6787 (2014) - [i5]Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich:
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. CoRR abs/1402.6964 (2014) - [i4]Austin R. Benson, Grey Ballard:
A Framework for Practical Parallel Fast Matrix Multiplication. CoRR abs/1409.2908 (2014) - 2013
- [c1]Austin R. Benson, David F. Gleich, James Demmel:
Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures. IEEE BigData 2013: 264-272 - [i3]Austin R. Benson, David F. Gleich, James Demmel:
Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures. CoRR abs/1301.1071 (2013) - [i2]Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, Lexing Ying:
A parallel directional Fast Multipole Method. CoRR abs/1311.4257 (2013) - [i1]Austin R. Benson, Sven Schmit, Robert Schreiber:
Silent error detection in numerical time-stepping schemes. CoRR abs/1312.2674 (2013)
Coauthor Index
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