The paper introduces methods to analyze community data using diffusion Frechet functions.
problem Analyzing complex ecosystems and their interactions.
method Developed methods for modeling and analyzing the organization of complex data across spatial scales.
result Introduced diffusion Frechet functions and vectors for robustly describing the shapes of probability distributions.
Deep learning identifies 30 common bacterial pathogens from Raman spectra.
problem Rapid and accurate identification of pathogenic bacteria.
method State-of-the-art deep learning applied to large Raman spectroscopy dataset.
result 99.0% accuracy in identifying 30 common bacterial pathogens.
Bayesian model fuses diverse microbiome data types.
problem Challenges in fusing different types of microbiome data.
method Flexible multinomial-Gaussian generative model with variational EM algorithm.
result Inferred latent variables provide common dimensionality reduction and predictive posterior distribution.
Paper uses machine learning to identify key pathways for c-di-GMP in bacterial genomes.
problem Understanding pathways essential for c-di-GMP in bacterial cellulose production.
method Applied Lasso and Random Forests for feature selection and modeling gene count data.
result Bacterial chemotaxis is identified as the most essential pathway for c-di-GMP encoding domains.
Deep learning automates bacterial image classification.
problem Manual bacterial classification is time-consuming and error-prone.
method ResNet-50 pre-trained CNN architecture with transfer learning.
result Average classification accuracy of 99.2%.
Machine learning identifies key metabolic control circuits in bacterial pathways.
problem Identifying regulated metabolic pathways in bacteria.
method Machine learning approach analyzing multi-omics data.
result Identification of E. coli Glycolysis regulatory circuits.
Logistic regression with wavelets achieves bacterial infection detection accuracy.
problem Interpreting complex biomedical signal models for high-stakes decisions.
method Wavelet features and knockoff variables for feature selection.
result Logistic regression outperforms neural networks in bacterial infection detection.
New method improves support estimation for unknown distributions.
problem Estimating the support size of an unknown distribution.
method Regularized Weighted Chebyshev Approximations, joint optimization of bias and variance, linear programming.
result Significant improvements in worst-case risk for synthetic data and accurate bacterial genus estimation for microbiome data.
Study uses machine learning to optimize antibiotic therapy for MRSA skin infections.
problem Optimizing antibiotic choice for MRSA skin infections due to reduced treatment options and side effects.
method Propensity score matching, machine learning models (SVM, RF, LASSO), counterfactual analysis.
result RF model shows stronger treatment heterogeneity and potential for therapy change.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the class…
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free…
Researchers use interpretable classifiers to predict antibiotic resistance.
problem Predicting antibiotic resistance from genome sequences.
method Set Covering Machines for highly interpretable models.
result Highly interpretable models for antibiotic resistance prediction.
Improved likelihood-free inference for high-dimensional models.
problem Challenges in likelihood-free inference for high-dimensional parameter spaces.
method Bayesian optimization-based approach with misspecification-robust characterisation.
result Efficient inference in 100-dimensional space with real data application.
ABC uses GPs to speed up estimating bacterial gene transfers.
problem Intractable likelihood in bacterial gene transfer models.
method Gaussian process modeling to approximate discrepancies.
result GP choice significantly affects ABC posterior accuracy.
New models analyze stability of gene regulation networks with coregulation.
problem Stability and structure of gene regulation networks with shared regulatory motifs.
method Developed formalism for modeling coregulation rules in RBN, analyzed stability through mean-field approach.
result Coregulation can increase network stability, especially in autoregulated multi-gene modules and hierarchical gene complexes.
New method detects changes in slope for time series data.
problem Detecting multiple changes in slope in univariate time series.
method CPOP: novel dynamic programming approach with L0 complexity penalty. result CPOP provides more reliable and parsimonious fits than competing methods.
HICODE detects hidden communities in social networks.
problem Weak, natural communities hidden within strong, dominant communities.
method HICODE (HIdden COmmunity DEtection) that identifies both hidden and dominant communities.
result HICODE outperforms state-of-the-art methods in uncovering both hidden and dominant communities.
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
We introduce community trees to summarize network structures.
problem Stability of community structures in networks.
method Clique percolation method (CPM) and persistent diagrams.
result Total star number (TSN) provides an upper bound on community tree changes.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
Unified method for discovering biclusters and triclusters in longitudinal data.
problem High-dimensional, sparsely sampled, irregularly observed longitudinal data.
method Tri-SfSVD, a unified sparse functional Singular Value Decomposition framework.
result Identified localized structures at the subject, subject-feature, and subject-feature-time levels.
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
Study exact community detection in k-community Gaussian mixtures with different intensities.
problem Community detection in k-community Gaussian mixtures with varying intensities.
method Explicitly find the threshold for exact recovery of maximum likelihood estimation.
result Threshold for exact recovery of maximum likelihood estimation is identified.
The paper introduces a method for detecting principal communities and embedding vertices.
problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.
Method detects shared and private communities in multilayer networks.
problem Detecting shared and private communities in multilayer networks.
method Variational Bayes approach for jointly inferring shared and unshared hidden communities.
result Our method outperforms state-of-the-art algorithms in detecting communities.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O(logT), improving significantly on full communication. vGraph learns community membership and node representation jointly.
problem Independent study of community detection and node representation learning limits graph analysis.
method vGraph is a probabilistic generative model that learns community membership and node representation collaboratively.
result vGraph outperforms many baselines in both community detection and node representation learning.
Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…
Study compares community detection methods in various networks.
problem Determine which community detection method is best for specific network types.
method Comprehensive empirical analysis of multiple methods on diverse network categories.
result Identifies different types of communities produced by various methods.
New GNN model detects overlapping communities better than existing methods.
problem Detecting overlapping communities in graphs.
method Graph Neural Network (GNN) for overlapping community detection.
result The proposed GNN model outperforms existing baselines significantly.
TMSCD detects multi-scale communities in temporal networks automatically.
problem Discovering multi-scale communities in large, evolving networks.
method Spectral multilayer formulation of MM method with automatic parameter selection.
result Automatic detection of multi-scale communities without manual parameter selection.
MACC learns communication protocols by adapting counterfactual reasoning.
problem Credit assignment and non-stationarity in communication environments.
method Adapts counterfactual reasoning to overcome credit assignment and uses action policy and Q-function of other agents to handle non-stationarity.
result MACC outperforms state-of-the-art baselines in four scenarios.
A novel framework for adaptive multi-agent communication in reinforcement learning.
problem Manual specification of communication structures in multi-agent reinforcement learning.
method Learning Structured Communication (LSC) framework using hierarchical graph neural networks.
result Adaptive hierarchical formations and efficient message propagation among agents.
Survey on community detection methods and their theoretical properties.
problem Consistent estimation of community labels in networks.
method Various community detection methods and their theoretical properties.
result Review of community detection methods and their theoretical properties.
Paper studies community detection in Degree-Corrected Block Models.
problem Community detection in networks.
method Derives asymptotic minimax risks and proposes an algorithm for consistent community detection.
result Shows how minimax risks depend on degree-correction parameters and network connectivities.
A framework for multi-agent communication over noisy channels in reinforcement learning.
problem Effective communication between multiple agents in a noisy environment.
method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.
result Jointly learned policies outperform separate learning of communication and decision making.
Generative model improves local community detection in networks.
problem Finding a single community in a large network using only a small part of it.
method Starting from a generative model for networks with community structure, approximating the unobserved parts to detect local communities.
result The proposed methods show comparable or improved results compared to state-of-the-art local community detection algorithms.
A new method reduces communication in distributed learning by skipping less informative gradient updates.
problem Efficient communication in distributed machine learning.
method Quantizes and skips less informative gradients to reduce communication overhead.
result Proves linear convergence rate similar to gradient descent with significant communication savings.
New metrics needed for better understanding emergent communication in multi-agent systems.
problem Current metrics for emergent communication are insufficient for complex environments.
method Training deep reinforcement learning agents in simple games to analyze communication effectiveness.
result Messages in emergent communication can be misleading and do not always impact the environment.
New method estimates number of communities in networks efficiently.
problem Estimating the number of communities in networks when it is unknown.
method Spectral properties of graph operators (non-backtracking matrix, Bethe Hessian matrix).
result Method is consistent and performs well under various models and parameters.
GRADE models evolving graph dynamics by learning node and community representations.
problem Lack of tools to study temporal community dynamics in evolving graphs.
method GRADE is a probabilistic model that learns evolving node and community representations via a random walk prior and variational inference.
result GRADE outperforms baselines in dynamic link prediction and dynamic community detection.
Proposes a new framework for detecting overlapping and non-overlapping communities.
problem Lack of methods for both overlapping and non-overlapping community detection.
method Integrated framework based on primary node criteria of internal and external association degrees.
result Outperforms existing methods on evaluation criteria.
Paper studies fundamental limits of communication in distributed learning.
problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.
New algorithm reduces communication traffic in decentralized learning.
problem Communication bottleneck in decentralized learning for low-bandwidth workers.
method Sparsification and adaptive peer selection to reduce communication traffic.
result Significant reduction in communication traffic compared to existing methods.
Study recovers community structure from coarse graph measurements.
problem Community recovery from low-resolution graph measurements.
method Formalized coarsening process of graph measurements, developed conditions for perfect recovery.
result Simple and closed-form asymptotic conditions for perfect recovery of coarse graph communities.
Paper reduces communication in distributed machine learning.
problem Reduces burdensome communication in distributed machine learning.
method Introduces communication-censoring technique to reduce transmissions of variables.
result CSGD algorithm achieves same convergence rate as SGD but with significant communication reduction.
New method detects overlapping communities in weighted graphs without pure nodes assumption.
problem Detect overlapping communities in weighted graphs without making pure nodes assumption.
method Convex optimization-based approach for weighted graphs.
result Success on artificial and real-world datasets.