Austin R. Benson (original) (raw)

• Nonlinear Feature Diffusion on Hypergraphs. Francesco Tudisco, Konstantin Prokopchik, Austin R. Benson. International Conference on Machine Learning (ICML), 2022.
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• fauci-email: a json digest of Anthony Fauci's released emails. Austin R. Benson, Nate Veldt, David F. Gleich. International Conference on Web and Social Media (ICWSM), 2022.
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• Diverse and Experienced Group Discovery via Hypergraph Clustering. Ilya Amburg, Nate Veldt, Austin R. Benson. SIAM Data Mining (SDM) 2022.
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• Hypergraph Cuts with General Splitting Functions. Nate Veldt, Austin R. Benson, Jon Kleinberg. SIAM Review (SIREV), 2022.
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• A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations. Junteng Jia, Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2022.
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• Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li. Neural Information Processing Systems (NeurIPS), 2022.
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• Graph-Based Methods for Discrete Choice. Kiran Tomlinson, Austin R. Benson. arXiv:2205.11365, 2022.
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• Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data. Darren C. Pagan, Calvin R. Pash, Austin R. Benson, Matthew P. Kasemer. arXiv:2205.06324, 2022.
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• Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. Nate Veldt, Austin R. Benson, Jon Kleinberg. Neural Information Processing Systems (NeurIPS), 2021.
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• Communication-efficient distributed eigenspace estimation. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. SIAM Journal on Mathematics of Data Science (SIMODS), 2021.
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• Generative hypergraph clustering: from blockmodels to modularity. Philip S. Chodrow, Nate Veldt, Austin R. Benson. Science Advances, 2021.
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• The Generalized Mean Densest Subgraph Problem. Nate Veldt, Austin R. Benson, Jon Kleinberg. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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• Choice Set Confounding in Discrete Choice. Kiran Tomlinson, Johan Ugander, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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• Learning Interpretable Feature Context Effects in Discrete Choice. Kiran Tomlinson, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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• Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. Derek Lim, Austin R. Benson. International Conference on Web and Social Media (ICWSM), 2021.
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• Higher-order Network Analysis Takes Off, Fueled by Old Ideas and New Data. Austin R. Benson, David F. Gleich, Desmond J. Higham. SIAM News, 2021.
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• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson. International Conference on Learning Representations (ICLR), 2021.
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• Planted Hitting Set Recovery in Hypergraphs. Ilya Amburg, Jon Kleinberg, Austin R. Benson. Journal of Physics: Complexity, 2021.
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• Nonlinear Higher-Order Label Spreading. Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik. The Web Conference (WWW), 2021.
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• Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis. Katherine Van Koevering, Austin R. Benson, Jon Kleinberg. The Web Conference (WWW), 2021.
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• Higher Order Information Identifies Tie Strength. Arnab Sarker, Jean-Baptiste Seby, Austin R. Benson, Ali Jadbabaie. arXiv:2108.02091, 2021.
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• Edge Proposal Sets for Link Prediction. Abhay Singh, Qian Huang, Sijia Linda Huang, Omkar Bhalerao, Horace He, Ser-Nam Lim, Austin R. Benson. arXiv:2106.15810, 2021.
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• Graph Belief Propagation Networks. Junteng Jia, Cenk Baykal, Vamsi K. Potluru, Austin R. Benson. arXiv:2106.03033, 2021.
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• Higher-order Homophily is Combinatorially Impossible. Nate Veldt, Austin R. Benson, Jon Kleinberg. arXiv:2103.11818, 2021.
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• Better Set Representations For Relational Reasoning. Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson. Neural Information Processing Systems (NeurIPS), 2020.
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• Entrywise Convergence of Iterative Methods for Eigenproblems. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. Neural Information Processing Systems (NeurIPS), 2020.
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• Residual Correlation in Graph Neural Network Regression. Junteng Jia, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
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• Minimizing Localized Ratio Cut Objectives in Hypergraphs. Nate Veldt, Austin R. Benson, Jon Kleinberg. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
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• Choice Set Optimization Under Discrete Choice Models of Group Decisions. Kiran Tomlinson, Austin R. Benson. International Conference on Machine Learning (ICML), 2020.
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• Neighborhood and PageRank methods for pairwise link prediction. Huda Nassar, Austin Benson, David F. Gleich. Social Network Analysis and Mining (SNAM), 2020
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• Network Interpolation. Thomas Reeves, Anil Damle, Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2020.
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• Measuring Directed Triadic Closure with Closure Coefficients. Hao Yin, Austin R. Benson, Johan Ugander. Network Science, 2020.
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• Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian. Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie. SIAM Review (SIREV), 2020.
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• Clustering in graphs and hypergraphs with categorical edge labels. Ilya Amburg, Nate Veldt, Austin R. Benson. The Web Conference (WWW), 2020.
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• Frozen Binomials on the Web: Word Ordering and Langauge Conventions in Online Text. Katherine Van Koevering, Austin R. Benson, Jon Kleinberg. The Web Conference (WWW), 2020.
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• Using cliques with higher-order spectral embeddings improves graph visualizations. Huda Nassar, Caitlin Kennedy, Shweta Jain, Caitlin Kennedy, Austin R. Benson, David F. Gleich. The Web Conference (WWW), 2020.
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• Retrieving Top Weighted Triangles in Graphs. Raunak Kumar, Paul Liu, Moses Charikar, Austin R. Benson. ACM International Conference on Web Search and Data Mining (WSDM), 2020.
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• Over-parametrized neural networks as under-determined linear systems. Austin R. Benson, Anil Damle, Alex Townsend. arXiv:2010.15959, 2020.
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• A simple bipartite graph projection model for clustering in networks. Austin R. Benson, Paul Liu, Hao Yin. arXiv:2007.00761, 2020.
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• Augmented Sparsifiers for Generalized Hypergraph Cuts with Applications to Decomposable Submodular Function Minimization. Austin R. Benson, Jon Kleinberg, Nate Veldt. arXiv:2007.08075, 2020.
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• Neural Jump Stochastic Differential Equations. Junteng Jia, Austin R. Benson. Neural Information Processing Systems (NeurIPS), 2019.
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• Modeling and Analysis of Tagging Networks in Stack Exchange Communities. Shangdi Yu, Xiang Fu, Austin R. Benson. Journal of Complex Networks, 2019.
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• Computing tensor Z-eigenvectors with dynamical systems. Austin R. Benson, David F. Gleich. SIAM Journal on Matrix Analysis and Applications (SIMAX), 2019.
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• Unsupervised learning of dislocation motion. Darren C. Pagan, Thien Q. Phan, Jordan S. Weaver, Austin R. Benson, Armand J. Beaudoin. Acta Materialia, 2019.
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• Automated Grain Yield Behavior Classification. Darren C. Pagan, Jakob Kaminsky, Wesley A. Tayon, Kelly E. Nygren, Armand J. Beaudoin, Austin R. Benson. The Journal of The Minerals, Metals & Materials Society (JOM), 2019.
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• Pairwise Link Prediction. Huda Nassar, Austin R. Benson, David F. Gleich. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019.Best Paper Award Runner-up.
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• Graph-based Semi-Supervised & Active Learning for Edge Flows. Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.
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• Network Density of States. Kun Dong, Austin R. Benson, David Bindel. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.Best Paper Award
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• Three hypergraph eigenvector centralities. Austin R. Benson. SIAM Journal on Mathematics of Data Science (SIMODS), 2019.
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• Link Prediction in Networks with Core-Fringe Data. Austin R. Benson, Jon Kleinberg. World Wide Web Conference (WWW), 2019.
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• Choosing to grow a graph: Modeling network formation as discrete choice. Jan Overgoor, Austin R. Benson, Johan Ugander. World Wide Web Conference (WWW), 2019.
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• Random Spatial Network Models with Core-Periphery Structure. Junteng Jia, Austin R. Benson. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
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• Sampling Methods for Counting Temporal Motifs. Paul Liu, Austin R. Benson, Moses Charikar. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
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• The Local Closure Coefficient: A New Perspective On Network Clustering. Hao Yin, Austin R. Benson, Jure Leskovec. ACM International Conference on Web Search and Data Mining (WSDM), 2019.
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• Incrementally Updated Spectral Embeddings. Vasileios Charisopoulos, Austin R. Benson, Anil Damle. arXiv:1909.01188, 2019.
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• Simplicial closure and higher-order link prediction. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
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• Found Graph Data and Planted Vertex Covers. Austin R. Benson, Jon Kleinberg. Neural Information Processing Systems (NeurIPS), 2018.
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• Sequences of Sets. Austin R. Benson, Ravi Kumar, Andrew Tomkins. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018.
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• Higher-order clustering in networks. Hao Yin, Austin R. Benson, Jure Leskovec. Physical Review E (PRE), 2018.
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• A Discrete Choice Model for Subset Selection. Austin R. Benson, Ravi Kumar, Andrew Tomkins. ACM International Conference on Web Search and Data Mining (WSDM), 2018.
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• Local higher-order graph clustering. Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
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• Motifs in temporal networks. Ashwin Paranjape, Austin R. Benson, Jure Leskovec. ACM International Conference on Web Search and Data Mining (WSDM), 2017.
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• The spacey random walk: a stochastic process for higher-order data. Austin R. Benson, David F. Gleich, Lek-Heng Lim. SIAM Review (SIREV), 2017.
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• Higher-order organization of complex networks. Austin R. Benson, David F. Gleich, Jure Leskovec. Science, 2016.
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• General tensor spectral co-clustering for higher-order data. Tao Wu, Austin R. Benson, David F. Gleich. Neural Information Processing Systems (NeurIPS), 2016.
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• On the relevance of irrelevant alternatives. Austin R. Benson, Ravi Kumar, Andrew Tomkins. International Conference on World Wide Web (WWW), 2016.
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• Modeling user consumption sequences. Austin R. Benson, Ravi Kumar, Andrew Tomkins. International Conference on World Wide Web (WWW), 2016.
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• Improving the numerical stability of fast matrix multiplication. Grey Ballard, Austin R. Benson, Alex Druinksy, Benjamin Lipshitz, Oded Schwartz. SIAM Journal on Matrix Analysis and Applications (SIMAX), 2016.
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• Tensor spectral clustering for partitioning higher-order network structures. Austin R. Benson, David F. Gleich, Jure Leskovec. SIAM International Conference on Data Mining (SDM), 2015.
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• A framework for practical parallel fast matrix multiplication. Austin R. Benson, Grey Ballard. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2015.
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• Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich. Neural Information Processing Systems (NeurIPS), 2014.Spotlight Presentation
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• Learning multifractal structure in large networks. Austin R. Benson, Carlos Riquelme, Sven Schmit. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014.
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• A parallel directional Fast Multipole Method. Austin R. Benson, Jack Poulson, Kenneth Tran, Björn Engquist, Lexing Ying. SIAM Journal on Scientific Computing (SISC), 2014.
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• Silent error detection in numerical time-stepping schemes. Austin R. Benson, Sven Schmit, Robert Schreiber. International Journal of High Performance Computing Applications (IJHPCA), 2014.
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• Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures. Austin R. Benson, David F. Gleich, James Demmel. IEEE International Conference on Big Data (IEEE BigData), 2013.
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