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 numerical methods and computational frameworks for analyzing and understanding large-scale and complex datasets. I often approach problems with some combination of network science, matrix and tensor computations, and machine learning.
[Curriculum Vitae]

My research is supported by the ARO and the NSF.

Expository material

• SIAM ALA '18 tutorial on Tensor Eigenvectors and Stochastic Processes (with David Gleich).
  [web] [slides] [code]

Cornell classes

• Spring 2019. CS 6241: Numerical Methods for Data Science.
  
• Fall 2018. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.

Preprints

• Neural Jump Stochastic Differential Equations.
  Junteng Jia and Austin R. Benson.
  arXiv:1905.10403, 2019.
  paper pdf

• Measuring Directed Triadic Closure with Closure Coefficients.
  Hao Yin, Austin R. Benson, and Johan Ugander.
  arXiv:1905.10683, 2019.
  paper pdf

• Planted Hitting Set Recovery in Hypergraphs.
  Ilya Amburg, Jon Kleinberg, and Austin R. Benson.
  arXiv:1905.05839, 2019.
  paper pdf code

• Network Interpolation.
  Thomas Reeves, Anil Damle, and Austin R. Benson.
  arXiv:1905.01253, 2019.
  paper pdf code

• Modeling and Analysis of Tagging Networks in Stack Exchange Communities.
  Xiang Fu*, and Shangdi Yu*, and Austin R. Benson (*equal contribution)
  arXiv:1902.02372, 2019.
  paper pdf code data

• Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian.
  Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, and Ali Jadbabaie.
  arXiv:1807.05044, 2018.
  paper pdf

• Computing tensor Z-eigenvectors with dynamical systems.
  Austin R. Benson and David F. Gleich.
  arXiv:1805.00903, 2018.
  paper pdf code

Peer-reviewed papers

• Pairwise Link Prediction.
  Huda Nassar, Austin R. Benson, and David F. Gleich.
  To appear in Advances in Social Network Analysis and Mining (ASONAM), 2019.
  

• Graph-based Semi-Supervised & Active Learning for Edge Flows.
  Junteng Jia, Michael T. Schaub, Santiago Segarra, and Austin R. Benson.
  To appear in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.
  paper pdf code

• Network Density of States.
  Kun Dong, Austin R. Benson, and David Bindel.
  To appear in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.
  paper pdf code

• Three hypergraph eigenvector centralities.
  Austin R. Benson.
  SIAM Journal on Mathematics of Data Science (SIMODS), 2019.
  paper pdf code

• Link Prediction in Networks with Core-Fringe Data.
  Austin R. Benson and Jon Kleinberg.
  To appear in Proceedings of the World Wide Web Conference (WWW), 2019.
  paper pdf code poster

• Choosing to grow a graph: Modeling network formation as discrete choice.
  Jan Overgoor, Austin R. Benson, and Johan Ugander.
  To appear in Proceedings of the World Wide Web Conference (WWW), 2019.
  paper pdf code poster

• Random Spatial Network Models with Core-Periphery Structure.
  Junteng Jia and Austin R. Benson.
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
  paper pdf code data poster

• Sampling Methods for Counting Temporal Motifs.
  Paul Liu, Austin R. Benson, and Moses Charikar.
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
  paper pdf code data

• The Local Closure Coefficient: A New Perspective On Network Clustering.
  Hao Yin, Austin R. Benson, and Jure Leskovec.
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.
  paper pdf

• 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

• Found Graph Data and Planted Vertex Covers.
  Austin R. Benson and Jon Kleinberg.
  Advances in Neural Information Processing Systems (NeurIPS), 2018.
  paper pdf code data poster

• Sequences of Sets.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018.
  paper pdf code data poster

• Higher-order clustering in networks.
  Hao Yin, Austin R. Benson, and Jure Leskovec.
  Physical Review E (PRE), 2018.
  paper pdf code

• 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.
  paper pdf code data

• 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 (SIREV), 2017.
  paper pdf code data

• Higher-order organization of complex networks.
  Austin R. Benson, David F. Gleich, and Jure Leskovec.
  Science, 2016.
  paper pdf supplement code data

• General tensor spectral co-clustering for higher-order data.
  Tao Wu, Austin R. Benson, and David F. Gleich.
  Advances in Neural Information Processing Systems (NeurIPS), 2016.
  paper pdf code

• 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.
  paper pdf

• Modeling user consumption sequences.
  Austin R. Benson, Ravi Kumar, and Andrew Tomkins.
  Proceedings of the International Conference on World Wide Web (WWW), 2016.
  paper pdf

• 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.
  paper pdf code

• 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.
  paper pdf code

• 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.
  paper pdf code

• 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.
  Advances in Neural Information Processing Systems (NeurIPS), 2014.
  Selected for spotlight presentation.
  paper pdf code data poster

• 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.
  paper pdf supplement

• 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.
  paper pdf code

• Silent error detection in numerical time-stepping schemes.
  Austin R. Benson, Sven Schmit, and Robert Schreiber.
  International Journal of High Performance Computing Applications (IJHPCA), 2014.
  paper pdf supplement code

• 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.
  paper pdf code

Theses

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