Broadly, my research focuses on developing numerical, computational, and algorithmic frameworks for analyzing complex data in an effort to better understand how the world is connected and how people make decisions.
[Curriculum Vitae]

My research is generously supported by the U.S. Army Research Office, JPMorgan Chase & Co., and the National Science Foundation.

News and events

• 07.09.2020. Dane Taylor and I co-chaired the SIAM Worksop on Network Science (NS20).
• 06.01.2020. At long last, Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian (with Michael Schaub, Paul Horn, Gabor Lippner, and Ali Jadbabaie) has been published in SIAM Review. Here is the editor's overview.
• 08.06.2019. I am very honored that Network Density of States (with Kun Dong and David Bindel) won the best paper award at KDD.
• 11.09.2018. Simplicial closure and higher-order link prediction (with Rediet Abebe, Michael Schaub, Ali Jadbabaie, and Jon Kleinberg) was published in PNAS. The Cornell Chronicle article Predicting future combos, from rap songs to pharmaceuticals has the highlights. Lots of new data from this paper is available here.

Upcoming and recent presentations

• 01.06.2021. Joint Mathematics Meetings.
• 10.23.2020. Texas A&M Data Science Institute.
• 07.21.2021. Kavli Frontiers of Science. [slides]
• 07.06.2020. Minisymposium on Applied and Computational Discrete Algorithms at SIAM Annual. [slides]
• 06.16.2020. Pattern Analysis for Networks and Network Generalizations (PANNG 2020) minisymposium, which was part of the inaugural SIAM Conference on Mathematics of Data Science. [slides]

Cornell classes

• Fall 2020. CS 6210: Matrix Computations.
  Office hours: 4:00PM–5:00PM on Wednesdays
• Spring 2020. CS 6241: Numerical Methods for Data Science.
• Fall 2019. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.
• Spring 2019. CS 6241: Numerical Methods for Data Science.
• Fall 2018. CS 2850/INFO 2040/ECON 2040/SOC 2090: Networks.

Expository material

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

Preprints

• Learning Interpretable Feature Context Effects in Discrete Choice.
  Kiran Tomlinson and Austin R. Benson.
  arXiv:2009.03417, 2020.
  paper pdf code data

• Communication-efficient distributed eigenspace estimation.
  Vasileios Charisopoulos, Austin R. Benson, and Anil Damle.
  arXiv:2009.02436, 2020.
  paper pdf code

• Augmented Sparsifiers for Generalized Hypergraph Cuts.
  Austin R. Benson, Jon Kleinberg, and Nate Veldt.
  arXiv:2007.08075, 2020.
  paper pdf

• A simple bipartite graph projection model for clustering in networks.
  Austin R. Benson, Paul Liu, and Hao Yin.
  arXiv:2007.00761, 2020.
  paper pdf code

• Fair Clustering for Diverse and Experienced Groups.
  Ilya Amburg, Nate Veldt, and Austin R. Benson.
  arXiv:2006.05645, 2020.
  paper pdf code data

• Nonlinear Higher-Order Label Spreading.
  Francesco Tudisco, Austin R. Benson, and Konstantin Prokopchik.
  arXiv:2006.04762, 2020.
  paper pdf code

• Hypergraph Cuts with General Splitting Functions.
  Nate Veldt, Austin R. Benson, and Jon Kleinberg.
  arXiv:2001.02817, 2020.
  paper pdf

• Incrementally Updated Spectral Embeddings.
  Vasileios Charisopoulos, Austin R. Benson, and Anil Damle.
  arXiv:1909.01188, 2019.
  paper pdf code poster

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

Peer-reviewed papers

• Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.
  Derek Lim and Austin R. Benson.
  To appear at ICWSM, 2021.
  paper pdf code data

• Better Set Representations For Relational Reasoning.
  Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, and Austin R. Benson.
  To appear at NeurIPS, 2020.
  paper pdf code

• Entrywise convergence of iterative methods for eigenproblems.
  Vasileios Charisopoulos, Austin R. Benson, and Anil Damle.
  To appear at NeurIPS, 2020.
  paper pdf code

• Residual Correlation in Graph Neural Network Regression.
  Junteng Jia and Austin R. Benson.
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
  paper pdf code data
  [(G)PyTorch implementation from Junwen Bai and Yucheng Lu.]

• Minimizing Localized Ratio Cut Objectives in Hypergraphs.
  Nate Veldt, Austin R. Benson, and Jon Kleinberg.
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
  paper pdf code data

• Choice Set Optimization Under Discrete Choice Models of Group Decisions.
  Kiran Tomlinson and Austin R. Benson.
  Proceedings of the International Conference on Machine Learning (ICML), 2020.
  paper pdf code data slides

• Neighborhood and PagRank methods for pairwise link prediction.
  Huda Nassar, Austin Benson, and David F. Gleich.
  Social Network Analysis and Mining (SNAM), 2020
  paper pdf code

• Network Interpolation.
  Thomas Reeves, Anil Damle, and Austin R. Benson.
  SIAM Journal on Mathematics of Data Science (SIMODS), 2020.
  paper pdf code

• Measuring Directed Triadic Closure with Closure Coefficients.
  Hao Yin, Austin R. Benson, and Johan Ugander.
  Network Science, 2020.
  paper pdf code

• Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian.
  Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, and Ali Jadbabaie.
  SIAM Review (SIREV), 2020.
  paper pdf
  [Editor's overview]

• Clustering in graphs and hypergraphs with categorical edge labels.
  Ilya Amburg, Nate Veldt, and Austin R. Benson.
  Proceedings of The Web Conference (WWW), 2020.
  paper pdf code data

• Frozen Binomials on the Web: Word Ordering and Langauge Conventions in Online Text.
  Katherine Van Koevering, Austin R. Benson, and Jon Kleinberg.
  Proceedings of The Web Conference (WWW), 2020.
  paper pdf code

• Using cliques with higher-order spectral embeddings improves graph visualizations.
  Huda Nassar, Caitlin Kennedy, Shweta Jain, Caitlin Kennedy, Austin R. Benson, and David F. Gleich.
  Proceedings of The Web Conference (WWW), 2020.
  paper pdf code video

• Retrieving Top Weighted Triangles in Graphs.
  Raunak Kumar,* Paul Liu,* Moses Charikar, and Austin R. Benson. (*equal contribution).
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2020.
  paper pdf code data

• Neural Jump Stochastic Differential Equations.
  Junteng Jia and Austin R. Benson.
  Advances in Neural Information Processing Systems (NeurIPS), 2019.
  paper pdf code poster

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

• Computing tensor Z-eigenvectors with dynamical systems.
  Austin R. Benson and David F. Gleich.
  SIAM Journal on Matrix Analysis and Applications (SIMAX), 2019.
  paper pdf code

• Unsupervised learning of dislocation motion.
  Darren C. Pagan, Thien Q. Phan, Jordan S. Weaver, Austin R. Benson, and Armand J. Beaudoin.
  Acta Materialia, 2019.
  paper pdf

• Automated Grain Yield Behavior Classification.
  Darren C. Pagan, Jakob Kaminsky, Wesley A. Tayon, Kelly E. Nygren, Armand J. Beaudoin, and Austin R. Benson.
  The Journal of The Minerals, Metals & Materials Society (JOM), 2019.
  paper pdf

• Pairwise Link Prediction.
  Huda Nassar, Austin R. Benson, and David F. Gleich.
  Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019.
  Best Paper Award Runner-up.
  paper pdf code

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

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

• 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.
  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.
  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 code

• 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 video

• 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 video

• 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 video

• 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 video

• 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