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Related papers: Computing loop corrections by message passing

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We consider an interacting system of spin variables on a loopy interaction graph, identified by a tree graph and a set of loopy interactions. We start from a high-temperature expansion for loopy interactions represented by a sum of…

Disordered Systems and Neural Networks · Physics 2015-09-23 A. Ramezanpour , S. Moghimi-Araghi

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the…

Statistical Mechanics · Physics 2023-05-04 Yijia Wang , Yuwen Ebony Zhang , Feng Pan , Pan Zhang

Many iterative and non-iterative methods have been developed for inverse problems associated with Ising models. Aiming to derive an accurate non-iterative method for the inverse problems, we employ the tree-reweighted approximation. Using…

Machine Learning · Statistics 2018-05-30 Takashi Sano

We introduce a method for computing corrections to Bethe approximation for spin models on arbitrary lattices. Unlike cluster variational methods, the new approach takes into account fluctuations on all length scales. The derivation of the…

Statistical Mechanics · Physics 2009-11-11 Andrea Montanari , Tommaso Rizzo

Message passing equations yield a sharp percolation transition in finite graphs, as an artifact of the locally treelike approximation. For an arbitrary finite, connected, undirected graph we construct an infinite tree having the same local…

Disordered Systems and Neural Networks · Physics 2017-05-03 Gábor Timár , Rui A. da Costa , Sergey N. Dorogovtsev , José F. F. Mendes

In this paper we offer a solution to a long-standing problem in the study of networks. Message passing is a fundamental technique for calculations on networks and graphs. The first versions of the method appeared in the 1930s and over the…

Social and Information Networks · Computer Science 2019-12-10 George T. Cantwell , M. E. J. Newman

We generalize the belief-propagation algorithm to sparse random networks with arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in these networks belongs to a given set of motifs (generalization of the configuration…

Disordered Systems and Neural Networks · Physics 2015-05-28 S. Yoon , A. V. Goltsev , S. N. Dorogovtsev , J. F. F. Mendes

We develop and analyze methods for computing provably optimal {\em maximum a posteriori} (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. By decomposing the original distribution into a convex…

Information Theory · Computer Science 2007-07-13 Martin J. Wainwright , Tommi S. Jaakkola , Alan S. Willsky

We derive the analytical expression for the first finite size correction to the average free energy of disordered Ising models on random regular graphs. The formula can be physically interpreted as a weighted sum over all non…

Disordered Systems and Neural Networks · Physics 2014-08-04 Carlo Lucibello , Flaviano Morone , Giorgio Parisi , Federico Ricci-Tersenghi , Tommaso Rizzo

Given a locally consistent set of reduced density matrices, we construct approximate density matrices which are globally consistent with the local density matrices we started from when the trial density matrix has a tree structure. We…

Disordered Systems and Neural Networks · Physics 2015-06-18 I. Biazzo , A. Ramezanpour

Network alignment generalizes and unifies several approaches for forming a matching or alignment between the vertices of two graphs. We study a mathematical programming framework for network alignment problem and a sparse variation of it…

Optimization and Control · Mathematics 2011-11-03 Mohsen Bayati , David F. Gleich , Amin Saberi , Ying Wang

We propose an algorithm to obtain numerically approximate solutions of the direct Ising problem, that is, to compute the free energy and the equilibrium observables of spin systems with arbitrary two-spin interactions. To this purpose we…

Statistical Mechanics · Physics 2019-11-20 Simona Cocco , Giancarlo Croce , Francesco Zamponi

Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm, or loopy belief-propagation. The exact solution to this problem is well known to be exponential in the…

Artificial Intelligence · Computer Science 2010-04-09 Julian J. McAuley , Tiberio S. Caetano

In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, a different way of obtaining the…

Artificial Intelligence · Computer Science 2007-06-01 Bastian Wemmenhove , Bert Kappen

Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes is included. However, node embeddings may be unable to…

Machine Learning · Computer Science 2024-03-29 Franka Bause , Samir Moustafa , Johannes Langguth , Wilfried N. Gansterer , Nils M. Kriege

Theoretical attempts proposed so far to describe ordinary percolation processes on real-world networks rely on the locally tree-like ansatz. Such an approximation, however, holds only to a limited extent, as real graphs are often…

Physics and Society · Physics 2016-03-30 Filippo Radicchi , Claudio Castellano

We study tree approximations to classical two-body partition functions on sparse and loopy graphs via the Brydges-Kennedy-Abdessalam-Rivasseau forest expansion. We show that for sparse graphs (with large cycles), the partition function…

Combinatorics · Mathematics 2024-02-08 Francesco Caravelli

The paper proposes a new message passing algorithm for cycle-free factor graphs. The proposed "entropy message passing" (EMP) algorithm may be viewed as sum-product message passing over the entropy semiring, which has previously appeared in…

Machine Learning · Computer Science 2016-11-18 Velimir M. Ilic , Miomir S. Stankovic , Branimir T. Todorovic

Probabilistic graphical models with frustration exhibit rugged energy landscapes that trap iterative optimization dynamics. These landscapes are shaped not only by local interactions, but crucially also by the global loop structure of the…

Disordered Systems and Neural Networks · Physics 2026-02-03 Timothee Leleu , Sam Reifenstein , Atsushi Yamamura , Surya Ganguli

Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs.…

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