You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 3 Nov 2021 • Alexander M. Mercier, Samuel V. Scarpino, Cristopher Moore

Network science has increasingly become central to the field of epidemiology and our ability to respond to infectious disease threats.

no code implementations • 1 Oct 2021 • George T. Cantwell, Cristopher Moore

Many datasets give partial information about an ordering or ranking by indicating which team won a game, which item a user prefers, or who infected whom.

no code implementations • 29 Jul 2021 • Varsha Dani, Josep Díaz, Thomas P. Hayes, Cristopher Moore

We give an algorithm that, if $r=n^\alpha$ for any $\alpha > 0$, with high probability reconstructs the vertex positions with a maximum error of $O(n^\beta)$ where $\beta=1/2-(4/3)\alpha$, until $\alpha \ge 3/8$ where $\beta=0$ and the error becomes $O(\sqrt{\log n})$.

no code implementations • 18 Dec 2019 • Mehrdad Moharrami, Cristopher Moore, Jiaming Xu

We study the problem of recovering a planted matching in randomly weighted complete bipartite graphs $K_{n, n}$.

no code implementations • 8 Apr 2019 • Alexander S. Wein, Ahmed El Alaoui, Cristopher Moore

Our hierarchy is analogous to the sum-of-squares (SOS) hierarchy but is instead inspired by statistical physics and related algorithms such as belief propagation and AMP (approximate message passing).

1 code implementation • 3 Sep 2017 • Caterina De Bacco, Daniel B. Larremore, Cristopher Moore

We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks.

no code implementations • 1 Feb 2017 • Cristopher Moore

While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a "ground truth" community structure built into the graph somehow.

Computational Complexity Statistical Mechanics Social and Information Networks Probability Physics and Society

1 code implementation • 5 Jan 2017 • Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, Cristopher Moore

In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately.

Social and Information Networks Statistical Mechanics Physics and Society

1 code implementation • 5 Apr 2016 • Antonia Godoy-Lorite, Roger Guimera, Cristopher Moore, Marta Sales-Pardo

With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important.

no code implementations • 19 Jun 2015 • Amir Ghasemian, Pan Zhang, Aaron Clauset, Cristopher Moore, Leto Peel

We study the fundamental limits on learning latent community structure in dynamic networks.

no code implementations • 29 Apr 2015 • Hyejin Youn, Logan Sutton, Eric Smith, Cristopher Moore, Jon F. Wilkins, Ian Maddieson, William Croft, Tanmoy Bhattacharya

How universal is human conceptual structure?

no code implementations • 30 Apr 2014 • Pan Zhang, Cristopher Moore, Lenka Zdeborová

For larger $k$ where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of $\alpha$.

1 code implementation • 23 Mar 2014 • Pan Zhang, Cristopher Moore

We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it.

no code implementations • 2 Dec 2013 • Greg Ver Steeg, Cristopher Moore, Aram Galstyan, Armen E. Allahverdyan

It predicts a first-order detectability transition whenever $q > 2$, while the finite-temperature cavity method shows that this is the case only when $q > 4$.

1 code implementation • 24 Jun 2013 • Florent Krzakala, Cristopher Moore, Elchanan Mossel, Joe Neeman, Allan Sly, Lenka Zdeborová, Pan Zhang

Spectral algorithms are classic approaches to clustering and community detection in networks.

no code implementations • 28 Mar 2013 • Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore

The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.

no code implementations • 17 Jul 2012 • Xiaoran Yan, Cosma Rohilla Shalizi, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, Lenka Zdeborova, Pan Zhang, Yaojia Zhu

We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.

no code implementations • 14 Sep 2011 • Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová

In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network.

Statistical Mechanics Disordered Systems and Neural Networks Social and Information Networks Physics and Society

no code implementations • 6 Feb 2011 • Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová

We present an asymptotically exact analysis of the problem of detecting communities in sparse random networks.

no code implementations • 9 Aug 2004 • Aaron Clauset, M. E. J. Newman, Cristopher Moore

Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure.

Statistical Mechanics Disordered Systems and Neural Networks

1 code implementation • 25 Jul 1997 • M. E. J. Newman, Cristopher Moore

Instead, it falls out of equilibrium at a temperature which decreases logarithmically as a function of the cooling time.

Statistical Mechanics

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.