Search Results for author: Cristopher Moore

Found 21 papers, 6 papers with code

Effective Resistance for Pandemics: Mobility Network Sparsification for High-Fidelity Epidemic Simulation

no code implementations3 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.


Belief propagation for permutations, rankings, and partial orders

no code implementations1 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.

Model Selection

Reconstruction of Random Geometric Graphs: Breaking the Omega(r) distortion barrier

no code implementations29 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 $O(\sqrt{\log n})$.

The Planted Matching Problem: Phase Transitions and Exact Results

no code implementations18 Dec 2019 Mehrdad Moharrami, Cristopher Moore, Jiaming Xu

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

The Kikuchi Hierarchy and Tensor PCA

no code implementations8 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).

Bayesian Inference

A physical model for efficient ranking in networks

1 code implementation3 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.

The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness

no code implementations1 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

Community detection, link prediction, and layer interdependence in multilayer networks

1 code implementation5 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

Accurate and scalable social recommendation using mixed-membership stochastic block models

1 code implementation5 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.

Collaborative Filtering

Phase transitions in semisupervised clustering of sparse networks

no code implementations30 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 $\alpha$.

Stochastic Block Model

Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity

1 code implementation23 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.

Stochastic Block Model

Phase Transitions in Community Detection: A Solvable Toy Model

no code implementations2 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 $q > 4$.

Community Detection

Scalable Text and Link Analysis with Mixed-Topic Link Models

no code implementations28 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.

Link Prediction Topic Classification

Model Selection for Degree-corrected Block Models

no code implementations17 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.

Model Selection Stochastic Block Model

Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications

no code implementations14 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

Phase transition in the detection of modules in sparse networks

no code implementations6 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.

Finding community structure in very large networks

no code implementations9 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

Glassy dynamics and aging in an exactly solvable spin model

1 code implementation25 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.