We apply network Lasso to semi-supervised regression problems involving network structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regressi…
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48 results for “network data analysis”
Develops rMultiNet R package for multilayer network analysis.
problem Analyzing multilayer network data.
method Two frameworks: MMSBM and MMLSM for generating multilayer networks; methods for node and layer embedding; clustering.
result Provides tools for multilayer network analysis.
A Neural Network Model to Classify Liver Cancer Patients Using Data Expansion and Compressionstat.ML
We develop a neural network model to classify liver cancer patients into high-risk and low-risk groups using genomic data. Our approach provides a novel technique to classify big data sets using neural network models. We preprocess the data before training the neural network models. We first expand the data using wavel…
There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work o…
New KNN test improves association analysis of high-dimensional sequencing data.
problem Challenges in using neural networks for high-dimensional sequencing data analysis.
method Kernel-based neural network (KNN) test for complex association analysis.
result KNN test outperforms SKAT in detecting non-linear and interaction effects.
Study disrupts Sicilian Mafia networks using data analysis.
problem Challenges in disrupting resilient criminal networks.
method Social Network Analysis methods applied to real-world datasets.
result Neutralizing only 5% of affiliates can disrupt network connectivity by 70%.
There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extensi…
This study uses persistent homology to analyze complex transitional networks from time series data.
problem Lack of effective tools to summarize complex topology in transitional networks.
method Persistent homology from topological data analysis applied to coarse-grained state-space networks (CGSSN).
result CGSSN improves dynamic state detection and noise robustness compared to other methods.
BSA reduces network data by interpreting feature subspaces.
problem Interpreting feature subspaces of unlabeled network data.
method Barycentric Subspace Analysis (BSA) for unlabeled networks.
result BSA provides a more interpretable approach compared to PCA.
In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wirele…
New algorithms sample random graph homomorphisms for network analysis.
problem Sampling random graph homomorphisms from a graph into a large network.
method Proposed two MCMC algorithms with bounds on mixing times and concentration.
result Network observables are stable under renormalized cut distance.
Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the latent variables given the data variables. However, it is not clear how to handl…
SurvCORN predicts survival curves using conditional ordinal ranking networks.
problem Challenges in survival analysis with censored data.
method SurvCORN: Conditional Ordinal Ranking Neural Network.
result SurvCORN improves accuracy in predicting time-to-event outcomes.
Study improves model estimation and variable selection using GANs with Lasso penalty.
problem Variable selection in high-dimensional data with deep networks.
method Conditional Wasserstein Generative Adversarial Networks with Group Lasso penalization.
result Established convergence rate for variable selection in censored survival data.
FNNs detect EEG signals without position dependence.
problem Detecting EEG signals without position dependence.
method Shift invariant functional neural networks (FNNs) using FDA methods.
result FNNs outperform FDA benchmarks in EEG classification.
Compressive Network Analysisstat.ML
Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for …
Physics-guided neural network improves power flow analysis.
problem Infeasibility of traditional numerical approaches due to outdated or unavailable PF equations.
method Proposes a physics-guided neural network to learn PF mappings from historical data while constraining by physical laws.
result Physics-guided neural network achieves better performance and generalizability than unconstrained data-driven approaches.
Paper proposes a deep learning model to predict stock prices using sentiment analysis.
problem Predicting future stock movement using financial textual and numerical data.
method A blending ensemble deep learning model with two levels of RNNs, LSTM, and GRU followed by a fully connected neural network.
result The model improves prediction accuracy compared to traditional methods.
A new method uses physics-informed neural networks to solve reliability analysis problems without simulations.
problem Solving reliability analysis problems without the need for expensive simulations.
method Physics-informed neural networks to learn directly from problem physics.
result Eliminates the need for expensive simulations and achieves highly accurate results.
Metaparametric neural networks improve survival analysis without prior knowledge.
problem Current neural networks restrict survival analysis to pre-determined times and fixed function shapes.
method Metaparametric neural network framework that extends existing methods to estimate generic functions.
result Metaparametric neural networks outperform state-of-the-art methods in capturing nonlinearities and identifying temporal patterns.
Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include ec…
Graph representation converts complex networks into vectors for easier analysis.
problem Efficient analysis of large network data.
method Introduces graph representation and network embedding models.
result Efficiently converts graph data into low-dimensional vectors.
Proposes a deep neural network for spatial data regression.
problem Regression of spatial data using deep neural networks.
method Localized two-layer deep neural network for spatial data, proving consistency and asymptotic convergence.
result Asymptotic convergence rate is faster than existing methods, demonstrating effectiveness on temperature estimation.
Neural networks trained with actor-critic algorithms converge to ODEs under weak convergence analysis.
problem Challenges in convergence analysis due to changing data distributions in online learning.
method Geometric ergodicity of data samples, Poisson equation, weak convergence techniques.
result Actor and critic networks converge to solutions of ODEs with random initial conditions.
A new method reduces data movement in neural network training.
problem Large data movement during neural network training.
method Streaming batch principal component analysis for low-rank updates.
result Effective training of convolutional neural networks with low overhead.
Study evaluates financial anomaly detection methods on Canadian stock market.
problem Detecting financial anomalies in the Canadian stock market.
method Topological data analysis (TDA), principal component analysis (PCA), and neural network-based approaches.
result Neural network-based methods achieve the strongest performance in detecting financial anomalies.
A new neural network reduces high-dimensional time-series data for faster classification.
problem Classifying high-dimensional time-series patterns efficiently.
method Developed a time-series discriminant component network (TSDCN) using TSDCA for dimensionality reduction and classification.
result The TSDCN achieves high-accuracy classification and reduces training time.
Networks: An Economic Perspectivephysics.soc-ph
We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one's behavior has on others' well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactio…
An important part of the information gathering and data analysis is to find out what people think about, either a product or an entity. Twitter is an opinion rich social networking site. The posts or tweets from this data can be used for mining people's opinions. The recent surge of activity in this area can be attribu…
Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks…
Develops a new tensor PCA method for analyzing multiple network data.
problem Analyzing multiple large networks for dimensionality reduction.
method Semi-Symmetric Tensor PCA (SS-TPCA) for principal components analysis.
result SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with error proportional to the square root of the number of vertices.
InVAErt networks use data-driven methods for system synthesis and identifiability analysis.
problem Model synthesis and identifiability analysis for complex systems.
method Deterministic encoder and decoder, normalizing flow, variational encoder, loss function penalty coefficients, latent space sampling.
result Validation through various system types, demonstrating effectiveness of the framework.
A new GNN architecture called coVariance neural network (VNN) improves stability and transferability of covariance matrix analysis.
problem Stability and transferability issues in covariance matrix analysis.
method Developed coVariance neural network (VNN) that operates on sample covariance matrices.
result VNN is more stable and transferable than PCA-based approaches.
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.
Paper analyzes Q-learning with neural networks, proving a fast convergence rate.
problem Analyzing the convergence rate of neural Q-learning.
method Finite-time analysis of neural Q-learning with a deep ReLU network.
result Neural Q-learning converges to optimal policy with rate.
Unified analysis of self-training for deep networks on unlabeled data.
problem Theoretical understanding of self-training for deep networks on unlabeled data.
method Unified theoretical analysis using expansion assumption and input-consistency regularization.
result Proves high accuracy of minimizers of population objectives based on self-training and input-consistency regularization.
Proposes QGC to distinguish between lower and upper tail connectivity in financial networks.
problem Identifying systemically important firms using financial data.
method Quantile Granger Causality (QGC) using Lasso penalized quantile regressions.
result QGC networks detect systemic risk more accurately than mean-based networks.
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networksstat.ML
SODEN uses neural networks and ODEs for scalable survival analysis.
problem Survival analysis with censored data and strong structural assumptions.
method Modeling survival distribution as an ODE, using adjoint sensitivity analysis for efficient optimization.
result Efficient estimation of survival models in large-scale applications.
Improved IFA with Generative Adversarial Networks for high-dimensional latent variables.
problem Limited expressiveness of traditional VAEs in high-dimensional latent variable modeling.
method Introducing Adversarial Variational Bayes (AVB) and Importance-weighted Adversarial Variational Bayes (IWAVB) algorithms.
result IWAVB demonstrated superior expressiveness and higher likelihood compared to IWAE.
TabSurv adapts tabular neural networks for survival analysis.
problem Survival analysis on tabular data using deep learning methods.
method Adapts modern tabular architectures to survival analysis using Weibull distribution or non-parametric prediction. Optimizes SurvHL histogram loss function.
result TabSurv consistently outperforms classical and deep learning baselines on 10 real-world survival datasets.
Paper examines adversarial attacks on event cause analysis in power grids.
problem Adversarial attacks manipulate data to mislead event classifiers.
method Investigated adversarial attacks on CNN-based event cause analysis.
result Demonstrated adversaries can misclassify events through stealthy data manipulations.
GT-PCA improves PCA for image and time series data.
problem Lack of robustness to transformations in PCA.
method GT-PCA is a neural network that estimates components invariant to specific transformations.
result GT-PCA outperforms alternative methods in synthetic and real data experiments.
Proposes LSTM for financial market trend forecasting.
problem Challenges in financial market trend forecasting.
method Uses LSTM for financial market trend forecasting.
result Improves performance compared to traditional methods.
Develops a method to estimate network difference in high-dimensional time series data.
problem Estimating network differences in high-dimensional data can be unreliable.
method Uses an L1 penalty on the difference of inverse spectral densities to estimate network differences.
result Establishes consistency of the method for sparse network differences.
Tutorial on using neural networks for single cell data analysis.
problem Handling large sequencing datasets efficiently.
method Single cell variational inference using variational auto-encoder.
result Model learns data distribution for insights.
The paper proposes a neural network model for estimating conditional mixture Weibull distributions with right-censored data.
problem Survival analysis with right-censored data in predictive maintenance and health fields.
method A neural network architecture is developed to estimate two-parameter Weibull distributions conditionally to features, extending to a finite mixture of Weibull distributions.
result The model outperforms state-of-the-art methods on real-world datasets and can consider any survival time horizon.
Simplified LSTM models improve sentiment analysis on Twitter debate data.
problem Performing sentiment analysis on long sequence data from Twitter debates.
method Developed six parameter-reduced LSTM models (slim LSTM) for faster training and reduced computational cost.
result Slim LSTM models outperform standard LSTM model in sentiment analysis of GOP Debate Twitter dataset.