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Related papers: Graphing methods for Kendall's {\tau}

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In a recent paper by the authors, a new approach--called the "embedding method"--was introduced, which allows to make use of exchangeable pairs for normal and multivariate normal approximation with Stein's method in cases where the…

Probability · Mathematics 2009-12-18 Gesine Reinert , Adrian Röllin

Canonical correlation analysis (CCA) is a widely used technique for estimating associations between two sets of multi-dimensional variables. Recent advancements in CCA methods have expanded their application to decipher the interactions of…

Machine Learning · Statistics 2025-02-05 Hongju Park , Shuyang Bai , Zhenyao Ye , Hwiyoung Lee , Tianzhou Ma , Shuo Chen

We study the statistics of edges and vertices in the vicinity of a reference vertex (origin) within random planar quadrangulations and Eulerian triangulations. Exact generating functions are obtained for theses graphs with fixed numbers of…

Statistical Mechanics · Physics 2010-04-05 J. Bouttier , P. Di Francesco , E. Guitter

Due to their flexibility to represent almost any kind of relational data, graph-based models have enjoyed a tremendous success over the past decades. While graphs are inherently only combinatorial objects, however, many prominent analysis…

Social and Information Networks · Computer Science 2024-03-27 Michaël Fanuel , Antoine Aspeel , Michael T. Schaub , Jean-Charles Delvenne

Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on…

Machine Learning · Statistics 2013-04-23 Mladen Kolar , Han Liu , Eric P. Xing

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more…

Machine Learning · Computer Science 2020-06-03 Fenxiao Chen , Yuncheng Wang , Bin Wang , C. -C. Jay Kuo

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensionality or "large p, small n" paradigms when p can be as…

Methodology · Statistics 2012-10-18 Gaorong Li , Heng Peng , Jun Zhang , Lixing Zhu

Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on…

Machine Learning · Statistics 2019-04-02 Aleksandar Bojchevski , Stephan Günnemann

Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry especially in sensor and IoT related research and applications, but few advances has been…

Machine Learning · Computer Science 2018-08-15 Lu Liu , Zhiguang Wang

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method…

Computation and Language · Computer Science 2020-01-24 Kun Xu , Linfeng Song , Yansong Feng , Yan Song , Dong Yu

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and…

Applications · Statistics 2023-01-11 Peter Wills , Francois G. Meyer

This research aims to improve the accuracy of complex volleyball predictions and provide more meaningful insights to coaches and players. We introduce a specialized graph encoding technique to add additional contact-by-contact volleyball…

Machine Learning · Computer Science 2023-08-23 Rhys Tracy , Haotian Xia , Alex Rasla , Yuan-Fang Wang , Ambuj Singh

This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree…

Information Theory · Computer Science 2018-04-10 Anders Host-Madsen , June Zhang

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as…

Human-Computer Interaction · Computer Science 2014-04-09 Bahador Saket , Paolo Simonetto , Stephen Kobourov , Katy Borner

This paper discusses the statistical inference problem associated with testing for dependence between two continuous random variables using Kendall's $\tau$ in the context of the missing data problem. We prove the worst-case identified set…

Statistics Theory · Mathematics 2022-02-25 Oliver R. Cutbill , Rami V. Tabri

We discuss a graph-based approach for testing spatial point patterns. This approach falls under the category of data-random graphs, which have been introduced and used for statistical pattern recognition in recent years. Our goal is to test…

Methodology · Statistics 2008-02-06 E. Ceyhan , C. E. Priebe , D. J. Marchette

Data analysts commonly utilize statistics to summarize large datasets. While it is often sufficient to explore only the summary statistics of a dataset (e.g., min/mean/max), Anscombe's Quartet demonstrates how such statistics can be…

Computational Geometry · Computer Science 2019-10-31 Hang Chen , Utkarsh Soni , Yafeng Lu , Vahan Huroyan , Ross Maciejewski , Stephen Kobourov

Graphs drawn in the plane are ubiquitous, arising from data sets through a variety of methods ranging from GIS analysis to image classification to shape analysis. A fundamental problem in this type of data is comparison: given a set of such…

Computational Geometry · Computer Science 2022-10-20 Levent Batakci , Abigail Branson , Bryan Castillo , Candace Todd , Erin Wolf Chambers , Elizabeth Munch

Tangles were originally introduced as a concept to formalize regions of high connectivity in graphs. In recent years, they have also been discovered as a link between structural graph theory and data science: when interpreting similarity in…

Statistics Theory · Mathematics 2024-03-12 Eva Fluck , Sandra Kiefer , Christoph Standke

Topology identification and inference of processes evolving over graphs arise in timely applications involving brain, transportation, financial, power, as well as social and information networks. This chapter provides an overview of graph…

Signal Processing · Electrical Eng. & Systems 2025-12-12 Gonzalo Mateos , Yanning Shen , Georgios B. Giannakis , Ananthram Swami
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