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 and understanding large-scale and complex datasets from the Web, social networks, biology, and other scientific domains. I usually approach problems with a combination of network science, matrix and tensor computations, and applied machine learning.
[Curriculum Vitae]

My research is supported by the ARO and the NSF.

News and events

07.16.2019. Along with Francesco Tudisco, Christine Klymko, and Eisha Natha, I am organizing a minisymposium on Mining and Modeling Evolving and Higher-Order Complex Data and Networks at ICIAM 2019.
11.09.2018. My research with Rediet Abebe, Michael Schaub, Ali Jadbabaie, and Jon Kleinberg on Simplicial closure and higher-order link prediction was published in PNAS. See the Cornell Chronicle article Predicting future combos, from rap songs to pharmaceuticals for the highlights.
06.07.2018. I organized a minisymposium on mining and modeling network data at SIAM Discrete Mathematics '18 in Denver. [web]

Upcoming and recent talks

07.16.2019. ICIAM '19
07.10.2019. ILAS '19
05.28.2019. Higher-Order Models in Network Science NetSci Satellite
05.27.2019. Statistical Inference for Network Models NetSci Satellite
04.25.2019. GraphEx [slides]
04.19.2019. Syracuse University [slides]
02.13.2019. WSDM '19 [slides]
02.08.2019. Clarkson Center for Complex Systems Science Seminar [slides]
10.30.2018. University at Buffalo Applied Mathematics Seminar [slides]
10.11.2018. Algorithms for Threat Detection Annual Workshop [slides]
09.10.2018. Cornell Scientific Computing and Numerics (SCAN) seminar [slides]
08.23.2018. KDD '18 [slides]
07.13.2018. SIAM Workshop on Network Science [slides]
06.07.2018. SIAM Discrete Mathematics [slides]
06.06.2018. Statistical Learning and Data Science / Nonparametric Statistics [slides]

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.
Office Hours: Tuesdays, 1:30pm–2:30pm, Gates 413B
Fall 2018. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.

Preprints

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, 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

Three hypergraph eigenvector centralities.
Austin R. Benson.
To appear in SIAM Journal on Mathematics of Data Science (SIMODS), 2019.
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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.
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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.
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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.
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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.
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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.
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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.
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Found Graph Data and Planted Vertex Covers.
Austin R. Benson and Jon Kleinberg.
Advances in 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.
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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.
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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.
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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.
Advances in 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.
Advances in 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 (IJHPCA), 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.
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