Austin R. Benson

Contact

arb@cs.cornell.edu
413B Bill & Melinda Gates Hall
@austinbenson
arbenson

Assistant Professor of Computer Science
Field Member of Applied Math
Cornell University

My research develops computational frameworks for analyzing large-scale and complex datasets coming from the Web, social networks, biology, and other scientific domains. This involves a combination of network science, matrix and tensor computations, data mining, machine learning, algorithm design, and high-performance computing.
My research is supported in part by the NSF.
[Curriculum Vitae]

• Spring 2019. CS 6241: Numerical Methods for Data Science.
• Fall 2018. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.
  Office Hours: Wednesdays, 9:00am–10:00am, Gates 203

Preprints

• Core-fringe link prediction.
  Austin R. Benson and Jon Kleinberg.
  arXiv:1811.11540, 2018.
  paper pdf code

• Choosing to grow a graph: Modeling network formation as discrete choice.
  Jan Overgoor, Austin R. Benson, and Johan Ugander.
  arXiv:1811.05008, 2018.
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• Three hypergraph eigenvector centralities.
  Austin R. Benson.
  arXiv:1807.09644, 2018.
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• Random Walks on Simplicial Complexes and the normalized Hodge Laplacian.
  Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie.
  arXiv:1807.05044, 2018.
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• Computing tensor Z-eigenvectors with dynamical systems.
  Austin R. Benson and David F. Gleich.
  arXiv:1805.00903, 2018.
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Peer-reviewed papers

• Simplicial closure and higher-order link prediction.
  Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg.
  Proceedings of the National Academy of Sciences (PNAS), 2018.
  paper pdf supplement code data

• Random Spatial Network Models with Core-Periphery Structure.
  Junteng Jia and Austin R. Benson.
  To appear in Proceedings of the 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, and Moses Charikar.
  To appear in Proceedings of the 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, and Jure Leskovec.
  To appear in Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
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• Found Graph Data and Planted Vertex Covers.
  Austin R. Benson and Jon Kleinberg.
  Proceedings of Neural Information Processing Systems (NeurIPS), 2018.
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• Sequences of Sets.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the 24th 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, and Jure Leskovec.
  Physical Review E (PRE), 2018.
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• A Discrete Choice Model for Subset Selection.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2018.
  paper pdf code data poster

• Local higher-order graph clustering.
  Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich.
  Proceedings of the 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*, and Jure Leskovec. (*equal contribution)
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2017.
  paper pdf code data poster

• The spacey random walk: a stochastic process for higher-order data.
  Austin R. Benson, David F. Gleich, and Lek-Heng Lim.
  SIAM Review, 2017.
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• Higher-order organization of complex networks.
  Austin R. Benson, David F. Gleich, and Jure Leskovec.
  Science, 2016.
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• General tensor spectral co-clustering for higher-order data.
  Tao Wu, Austin R. Benson, and David F. Gleich.
  Proceedings of Neural Information Processing Systems (NeurIPS), 2016.
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• On the relevance of irrelevant alternatives.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the International Conference on World Wide Web (WWW), 2016.
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• Modeling user consumption sequences.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the 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, and 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, and Jure Leskovec.
  Proceedings of the SIAM International Conference on Data Mining (SDM), 2015.
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• A framework for practical parallel fast matrix multiplication.
  Austin R. Benson and Grey Ballard.
  Proceedings of the 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, and David F. Gleich.
  Proceedings of Neural Information Processing Systems (NeurIPS), 2014.
  Selected for spotlight presentation.
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• Learning multifractal structure in large networks.
  Austin R. Benson, Carlos Riquelme, and Sven Schmit.
  Proceedings of the 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, and 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, and Robert Schreiber.
  International Journal of High Performance Computing Applications, 2014.
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• Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures.
  Austin R. Benson, David F. Gleich, and James Demmel.
  Proceedings of the IEEE International Conference on Big Data (IEEE BigData), 2013.
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Theses

• Tools for higher-order network analysis.
  Austin Reilley Benson.
  Stanford PhD thesis, 2017.
  Stanford ICME Gene Golub Doctoral Dissertation Award winner.
  paper pdf