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In this paper, we extend the analysis of random Kronecker graphs to multi-dimensional networks represented as tensors, enabling a more detailed and nuanced understanding of complex network structures. We decompose the adjacency tensor of…

Numerical Analysis · Mathematics 2025-06-30 Sanaa Khobizy

Tensor-valued data arise naturally in neuroimaging, genomics, climate science, and spatiotemporal networks, where multilinear dependencies across modes carry information that is destroyed under vectorization. Existing approaches either…

Machine Learning · Statistics 2026-05-20 Elynn Chen , Jiayu Li , Zheshi Zheng , Jian Pei

The critical point of a topological phase transition is described by a conformal field theory, where finite-size corrections to energy are uniquely related to its central charge. We investigate the finite-size scaling away from criticality…

Statistical Mechanics · Physics 2016-01-18 Tobias Gulden , Michael Janas , Yuting Wang , Alex Kamenev

The recently developed tensor renormalization-group (TRG) method provides a highly precise technique for deriving thermodynamic and critical properties of lattice Hamiltonians. The TRG is a local coarse-graining transformation, with the…

Statistical Mechanics · Physics 2008-02-18 Michael Hinczewski , A. Nihat Berker

The quantum $5$-state Potts model is known to possess a perturbative description using complex conformal field theory (CCFT), the analytic continuation of ``theory space" to a complex plane. To study the corresponding complex fixed point on…

Strongly Correlated Electrons · Physics 2026-04-01 Vic Vander Linden , Boris De Vos , Kevin Vervoort , Frank Verstraete , Atsushi Ueda

Through coarse-graining, tensor network representations of a two-dimensional critical lattice model flow to a universal four-leg tensor, corresponding to a conformal field theory (CFT) fixed-point. We computed explicit elements of the…

High Energy Physics - Theory · Physics 2023-08-07 Atsushi Ueda , Masahito Yamazaki

We study the classical two-dimensional $\mathrm{RP^2}$ and Heisenberg models, using the Tensor-Network Renormalization (TNR) method. The determination of the phase diagram of these models has been challenging and controversial, owing to the…

Statistical Mechanics · Physics 2024-01-05 Atsushi Ueda , Masaki Oshikawa

In quantum spin chains at criticality, two types of scaling for the entanglement entropy exist: one comes from conformal field theory (CFT), and the other is for entanglement support of matrix product state (MPS) approximation. They…

Statistical Mechanics · Physics 2011-09-02 Hiroaki Matsueda

We determine the scaling functions describing the crossover from Ising-like critical behavior to classical critical behavior in two-dimensional systems with a variable interaction range. Since this crossover spans several decades in the…

Statistical Mechanics · Physics 2009-10-30 Erik Luijten , Henk W. J. Blöte , Kurt Binder

The decomposition of correlation functions into conformal blocks is an indispensable tool in conformal field theory. For spinning correlators, non-trivial tensor structures are needed to mediate between the conformal blocks, which are…

High Energy Physics - Theory · Physics 2020-10-28 Ilija Buric , Mikhail Isachenkov , Volker Schomerus

We develop coarse-graining tensor renormalization group algorithms to compute physical properties of two-dimensional lattice models on finite periodic lattices. Two different coarse-graining strategies, one based on the tensor…

Strongly Correlated Electrons · Physics 2016-07-12 Hui-Hai Zhao , Zhi-Yuan Xie , Tao Xiang , Masatoshi Imada

Tensor decomposition plays a key role in identifying common features across a collection of matrices in many areas of science. A fundamental need in big data research is to process data tabulated as large-scale matrices using eigenvectors.…

Computational Engineering, Finance, and Science · Computer Science 2016-05-24 HyungSeon Oh

Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensive comparison between classical TNs and TN-inspired quantum…

Quantum Physics · Physics 2022-12-20 Jack Y. Araz , Michael Spannowsky

In this paper we explore the practical use of the corner transfer matrix and its higher-dimensional generalization, the corner tensor, to develop tensor network algorithms for the classical simulation of quantum lattice systems of infinite…

Strongly Correlated Electrons · Physics 2012-05-11 Roman Orus

We derive a composite centrality measure for general weighted and directed complex networks, based on measure standardisation and invariant statistical inheritance schemes. Different schemes generate different intermediate abstract measures…

Methodology · Statistics 2014-01-21 Andreas Joseph , Guanrong Chen

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of…

Physics and Society · Physics 2021-09-15 Dan Chen , Housheng Su , Xiaofan Wang , Gui-Jun Pan , Guanrong Chen

An interface connecting two distinct conformal field theories hosts rich critical behaviors. In this work, we investigate the entanglement properties of such critical interface theories for probing the underlying universality. As inspired…

Statistical Mechanics · Physics 2024-01-10 Qicheng Tang , Zixia Wei , Yin Tang , Xueda Wen , W. Zhu

We propose a novel approach to study conformal field theories (CFTs) in general dimensions. In the conformal bootstrap program, one usually searches for consistent CFT data that satisfy crossing symmetry. In the new approach, we reverse the…

High Energy Physics - Theory · Physics 2018-01-19 Wenliang Li

We use the formalism of strange correlators to construct a critical classical lattice model in two dimensions with the \emph{Haagerup fusion category} $\mathcal{H}_3$ as input data. We present compelling numerical evidence in the form of…

An algorithm of the tensor renormalization group is proposed based on a randomized algorithm for singular value decomposition. Our algorithm is applicable to a broad range of two-dimensional classical models. In the case of a square…

Statistical Mechanics · Physics 2018-03-23 Satoshi Morita , Ryo Igarashi , Hui-Hai Zhao , Naoki Kawashima