Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper present…
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48 results for “multilayer method”
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.
Inverse design of multilayer nanoparticles using artificial neural networks and genetic algorithmeess.SP
New method designs multilayer nanoparticles using AI.
problem Difficult to design multilayer nanoparticles by trial and error.
method Combines genetic algorithm and neural network for inverse design.
result Successfully designs multilayer nanoparticles efficiently.
We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre-trained input. The proposed method effectively uses rich input information in the earlier layer for robust leaning and revising internal representat…
Improved graph-based multiclass classification for multilayer data.
problem Efficient classification of multilayer data with limited labeled examples.
method Generalized diffuse interface methods applied to multilayer graphs, using spectral decomposition and fast matrix-vector products.
result Highly scalable and efficient classification for large, high-dimensional data sets.
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks. The multilayer network is assumed to follow a generative probabilistic model that takes into account the similarities and dissimilarities between the communities. We make use of…
New integral transforms solve multilayer heat equations.
problem Solving multilayer heat equations with moving boundaries.
method Expanding Dirac delta function in eigenfunctions, constructing oscillating integral transforms.
result Semi-analytical solutions for various problems.
We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of matrix means that incl…
New algorithm assesses credit risk in multilayer networks over time.
problem Quantifying evolving credit risk in complex, interconnected networks.
method Personalized PageRank algorithm for multilayer networks.
result Credit risk evolves and propagates through multilayer networks over time.
A framework for multilayer networks predicts links without shared structures.
problem Link prediction in multilayer networks without shared structures.
method Bi-level model averaging with -fold cross-validation.
result Framework outperforms existing methods in predictive accuracy and robustness.
New method constructs multilayer networks from financial data, capturing dependencies across different risk factors.
problem Difficult construction of multilayer networks, neglecting time delays and interdependencies.
method Tucker tensor autoregression for direct multilayer network construction.
result Captures within and between connections, identifies strong interconnections between volumes and prices layers.
Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to…
Novel model detects communities in noisy multilayer networks.
problem Understanding communities in noisy multilayer networks.
method Hierarchical variational inference for joint detection and typologizing.
result Discover communities of subjects with co-occurrent psychopathologies.
Flexible inference model for multilayer networks with heterogeneous data.
problem Complexity of heterogeneous data in networked datasets.
method Probabilistic generative model using Bayesian framework and Laplace matching.
result Effective detection of overlapping community structures and prediction tasks.
New method clusters multilayer graphs with missing nodes.
problem Clustering multilayer graphs with missing nodes.
method Proposes a new framework for multilayer graphs with missing nodes, extending clustering methods to handle incomplete data.
result Proves consistency results under the Multi-Layer Stochastic Block Model assumption and highlights promising performance on real datasets.
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theo…
Develops ML method for solving financial equations.
problem Solving financial equations efficiently and accurately.
method Combines semi-analytical and numerical techniques.
result Significantly faster and more accurate solutions.
Many recent developments in network analysis have focused on multilayer networks, which one can use to encode time-dependent interactions, multiple types of interactions, and other complications that arise in complex systems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale fe…
A novel multilayer network approach for text analysis.
problem Clustering documents and finding topics in large collections with metadata and hyperlinks.
method Multilayer Networks and Stochastic Block Models applied to multiple data types.
result Taking into account multiple types of information improves topic and document clustering.
Paper introduces TSSDMN for modeling dynamic multilayer networks.
problem Capturing temporal and cross-layer dynamics in multilayer networks.
method Tensor State Space Model (TSSDMN) using symmetric Tucker decomposition.
result TSSDMN uniquely captures temporal dynamics within and across layers.
Unified framework detects dynamic community structure in brain networks across individuals.
problem Detecting community structure in functional brain networks across multiple subjects and over time.
method Markov-switching stochastic block model (MSS-SBM) for multilayer brain networks.
result Captures dynamic reconfiguration of modular connectivity in brain networks across different task conditions.
A network community detection method with integration of data from multiple layers and node attributesphysics.soc-ph
A method for community detection in multilayer networks using data matrices.
problem Community detection in multilayer networks with various node attributes.
method Data matrix representation and regular decomposition method extension for compression.
result Method identifies community structures well-aligned with real-world network hierarchies.
Neural NMF discovers hierarchical topics in multilayer data.
problem Detecting latent hierarchical structure in multilayer data.
method Recursive application of nonnegative matrix factorization (NMF) in layers with backpropagation optimization.
result Neural NMF outperforms other hierarchical NMF methods in synthetic and real-world datasets.
ALMA improves clustering of multilayer networks.
problem Clustering multilayer networks with distinct layers and communities.
method Alternating minimization algorithm (ALMA) for simultaneous layer partition and community estimation.
result ALMA achieves higher accuracy than TWIST in clustering multilayer networks.
Community detection in temporal multilayer networks, with an application to correlation networksphysics.soc-ph
Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilay…
We propose a new type of hidden layer for a multilayer perceptron, and demonstrate that it obtains the best reported performance for an MLP on the MNIST dataset.
SDP approach recovers communities in multilayer hypergraphs from aggregated similarity matrices.
problem Community recovery in multilayer hypergraphs using aggregated similarity matrices.
method Semidefinite programming (SDP) approach.
result Information-theoretic conditions for exact recovery in both assortative and disassortative cases.
A new method clusters data from multiple sources using a mixture of multilayer SBMs.
problem Aggregating multiple clustering results from different data sources.
method Uses a mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices.
result Identifies and clusters observations based on their specificities within components.
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groupscs.LG
New method constructs equivariant neural networks for arbitrary matrix groups.
problem Challenges in constructing equivariant neural networks for complex groups.
method Completely general algorithm for solving equivariant layers of matrix groups.
result Constructs multilayer perceptrons equivariant to multiple groups including O(1,3), O(5), Sp(n), and Rubik's cube group.
Global convergence of multilayer neural networks proven for any depth.
problem Global convergence of multilayer neural networks in the mean field regime.
method Mean field limit framework, neuronal embedding, bidirectional diversity condition.
result Global convergence for multilayer networks of any depths, including correlated initializations.
Proposes a new algorithm to estimate invariant subspaces across multilayer networks.
problem Estimating invariant subspaces across heterogeneous multiple networks.
method Bias-corrected joint spectral embedding algorithm that recursively calibrates diagonal bias and iteratively updates the subspace estimator.
result Established entrywise subspace perturbation bound and entrywise eigenvector central limit theorem for the algorithm.
This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction mode…
FVI method calculates bicausal OT with neural networks, outperforming other methods.
problem Computing bicausal optimal transport with adapted coupling structures.
method FVI method using multilayer neural networks to approximate value functions.
result FVI method outperforms linear programming and Sinkhorn methods in scalability.
Model visualizes and analyzes multilayer networks in a latent space.
problem Characterize multiple social networks over a common set of actors.
method Bayesian statistical model with hierarchical prior distribution.
result Visualizes multilayer network data in a low-dimensional Euclidean space.
Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with r…
This work studies fluctuation in multilayer neural networks using mean field theory.
problem Understanding fluctuation in multilayer neural networks with mean field training.
method Developed a second-order mean field limit to capture fluctuation, demonstrating stability of gradient descent training.
result Gradient descent training in multilayer networks biases towards minimal fluctuation, even after convergence.
Modeling structure and credit risk of the economy: a multilayer bank-firm network approachphysics.soc-ph
Develops a framework to assess systemic risk in the economy using bank-firm network data.
problem Measuring systemic risk in the economy using multilayer network data.
method Unified framework combining techniques to reconstruct multilayer economy structure from bank and firm balance sheets, and dynamics of shock propagation.
result Identifies systemically important firms and banks, and assesses systemic risk determinants.
Proposes using MLP for predicting optimal penalty in changepoint detection.
problem Predicting optimal penalty for changepoints in sequences.
method Uses a multilayer perceptron (MLP) with ReLU activation function to predict penalty.
result Improves accuracy and F1 score compared to existing models.
We develop a mathematically rigorous framework for multilayer neural networks in the mean field regime. As the network's widths increase, the network's learning trajectory is shown to be well captured by a meaningful and dynamically nonlinear limit (the \textit{mean field} limit), which is characterized by a system of …
Multilayer graphs encode different kind of interactions between the same set of entities. When one wants to cluster such a multilayer graph, the natural question arises how one should merge the information different layers. We introduce in this paper a one-parameter family of matrix power means for merging the Laplacia…
This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of t…
PAC-Bayesian bounds for MLPs with cross entropy loss validated.
problem Generalization bounds for MLPs with cross entropy loss.
method Introduced probabilistic explanations and proved PAC-Bayesian bounds using ELBO.
result MLPs with cross entropy loss inherently guarantee PAC-Bayesian generalization bounds.
Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multi…
New method breaks symmetry in neural networks, improving sample efficiency.
problem Symmetry in neural networks limits their ability to learn unique features.
method Introduces 'relaxed equivariance' to overcome symmetry limitations.
result Equivariant multilayer perceptrons (E-MLPs) can now break symmetry at the sample level.
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transpar…
Looped Transformers improve robustness and expressivity in in-context learning for diverse tasks.
problem Improving robustness and expressivity in in-context learning for diverse tasks.
method Study in-context linear regression with diverse tasks, focusing on depth and looping.
result Looped Transformers exhibit similar expressive power and are provably robust under mild assumptions.
We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable f…
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…