Search Results - "Benson, Austin R."

Higher-order organization of complex networks by Benson, Austin R., Gleich, David F., Leskovec, Jure

“…Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many…”
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Simplicial closure and higher-order link prediction by Benson, Austin R., Abebe, Rediet, Schaub, Michael T., Jadbabaie, Ali, Kleinberg, Jon

“…Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems…”
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Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data by Pagan, Darren C., Pash, Calvin R., Benson, Austin R., Kasemer, Matthew P.

“…Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN…”
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Generative hypergraph clustering: From blockmodels to modularity by Chodrow, Philip S., Veldt, Nate, Benson, Austin R.

“…Novel clustering techniques enable the detection of modules in large datasets with multiway interactions. Hypergraphs are a natural modeling paradigm for…”
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Combinatorial characterizations and impossibilities for higher-order homophily by Veldt, Nate, Benson, Austin R, Kleinberg, Jon

“…Homophily is the seemingly ubiquitous tendency for people to connect and interact with other individuals who are similar to them. This is a well-documented…”
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Planted hitting set recovery in hypergraphs by Amburg, Ilya, Kleinberg, Jon, Benson, Austin R

“…Abstract In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes. This results in a…”
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Silent error detection in numerical time-stepping schemes by Benson, Austin R, Schmit, Sven, Schreiber, Robert

“…Errors due to hardware or low-level software problems, if detected, can be fixed by various schemes, such as recomputation from a checkpoint. Silent errors are…”
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Neighborhood and PageRank methods for pairwise link prediction by Nassar, Huda, Benson, Austin R., Gleich, David F.

“…Link prediction is a common problem in network science that cuts across many disciplines. The goal is to forecast the appearance of new links or to find links…”
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Hypergraph patterns and collaboration structure by Juul, Jonas L., Benson, Austin R., Kleinberg, Jon

“…Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external…”
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Modelling and analysis of tagging networks in Stack Exchange communities by Fu, Xiang, Yu, Shangdi, Benson, Austin R

“…Abstract Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behaviour on such…”
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Automated Grain Yield Behavior Classification by Pagan, Darren C., Kaminsky, Jakob, Tayon, Wesley A., Nygren, Kelly E., Beaudoin, Armand J., Benson, Austin R.

“…A method for classifying grain stress evolution behaviors using unsupervised learning techniques is presented. The method is applied to analyze grain stress…”
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Pairwise Link Prediction by Nassar, Huda, Benson, Austin R., Gleich, David F.

“…Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links…”
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The Spacey Random Walk: A Stochastic Process for Higher-Order Data by Benson, Austin R., Gleich, David F., Lim, Lek-Heng

“…Random walks are a fundamental model in applied mathematics and are a common example of a Markov chain. The limiting stationary distribution of the Markov…”
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Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures by Benson, Austin R., Gleich, David F., Demmel, James

“…The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices…”
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Unsupervised learning of dislocation motion by Pagan, Darren C., Phan, Thien Q., Weaver, Jordan S., Benson, Austin R., Beaudoin, Armand J.

“…[Display omitted] The unsupervised learning technique, locally linear embedding (LLE), is applied to the analysis of X-ray diffraction data measured in-situ…”
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Graph-Based Methods for Discrete Choice by Tomlinson, Kiran, Benson, Austin R

“…Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share,…”
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A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations by Jia, Junteng, Benson, Austin R

“…Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and…”
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Learning Interpretable Feature Context Effects in Discrete Choice by Tomlinson, Kiran, Benson, Austin R

“…The outcomes of elections, product sales, and the structure of social connections are all determined by the choices individuals make when presented with a set…”
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Residual Correlation in Graph Neural Network Regression by Jia, Junteng, Benson, Austin R

“…KDD 2020 A graph neural network transforms features in each vertex's neighborhood into a vector representation of the vertex. Afterward, each vertex's…”
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Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform by Lim, Derek, Benson, Austin R

“…Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary…”
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