Paper proposes a model to integrate diverse drug features for accurate similarity measures.
problem Challenges in integrating heterogeneous, noisy, nonlinear-related drug features.
method Attentive Multi-view Graph Auto-Encoders with flexible design for semi-supervised and unsupervised settings.
result Significant predictive accuracy improvement and better interpretability.
Proposes a new multi-view graph learning framework to model consistency and inconsistency.
problem Graph learning methods often neglect inconsistency across multiple views, making them vulnerable to noisy datasets.
method Proposes a unified objective function to simultaneously model consistency and inconsistency, iteratively learning consistent and unified graphs.
result Demonstrates robustness and efficiency of the proposed approach on twelve multi-view datasets.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
problem Inconsistency in multi-view data affects clustering performance.
method Integrates consistent and inconsistent parts of multiple views using a unified matrix.
result Demonstrates improved clustering performance on real-world datasets.
C-RSP embeds multi-view graphs using randomized shortest paths.
problem Combining multiple views of a graph to improve inference quality.
method C-RSP algorithm that generates a common embedding using RSP.
result C-RSP outperforms benchmarks in embedding and clustering tasks.
Proposes a method for multi-view clustering that integrates consistent and complementary graph regularizers.
problem Multi-view clustering where views have both consistent and complementary information.
method Consistent and complementary graph-regularized multi-view subspace clustering (GRMSC).
result The proposed method outperforms state-of-the-art methods on benchmark datasets.
Proposes a deep Auto-Encoder-like framework for visual-tactile fusion object clustering.
problem Combining visual and tactile information for better object clustering.
method Deep Auto-Encoder-like Non-negative Matrix Factorization framework, graph regularizer, modality-level consensus regularizer, alternating minimization strategy.
result Improves object clustering performance by leveraging both visual and tactile modalities.
M2E embeds multi-view multi-graph brain networks for better clustering.
problem Clustering brain networks from multiple views to understand disease mechanisms.
method Stack multi-graphs into tensors, use tensor techniques to leverage multi-view and multi-graph interactions.
result M2E outperforms existing methods on clustering brain networks.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.
Enhances robustness of multi-view clustering via partition fusion.
problem Dealing with noises and inconsistency in multi-view data.
method Generates multiple partitions, integrates them, and co-evolves graph learning, partition generation, and view weight learning.
result Empirical results verify the effectiveness and robustness of the proposed approach.
Proposes LMSSC for multi-view semi-supervised classification.
problem Leveraging multiple complementary views for improved classification.
method Semi-supervised classification with latent multi-view representation learning.
result Unified framework for latent representation learning, graph construction, and label propagation.
Paper proposes a new method for joint feature selection and graph learning.
problem Previous methods suffer from neglecting joint formulation and lack of graph learning.
method Formulates multi-view feature selection with orthogonal decomposition, incorporates cross-space locality preservation, and uses a unified objective function for simultaneous learning.
result Demonstrates superior performance in multi-view feature selection and graph learning tasks.
A novel multi-view spectral clustering model fuses and clusters data views.
problem Fusing and clustering multi-view data effectively.
method Simultaneously fuses and clusters views into a single graph.
result The proposed method outperforms existing techniques.
DGA and DVGA learn disentangled graph representations to improve graph analysis.
problem Holistic graph auto-encoders fail to capture latent factors effectively.
method Design disentangled graph convolutional network and component-wise flow, impose independence constraints.
result Improved disentangled graph representations enhance graph analysis tasks.
Two graph auto-encoders decouple feature propagation from graph convolution layers.
problem Designing efficient graph auto-encoders with fixed receptive fields.
method L-GAE and L-VGAE using linear matrix computation before auto-encoder input.
result Comparable performance to VGAEs with smaller, simpler networks.
Develops a graph-based convolutional network for multi-view networks to improve poverty research.
problem Binary treatment of social network relations in graph learning models.
method Multi-GCN: Graph Convolutional Networks for Multi-View Networks.
result Multi-GCN outperforms state-of-the-art algorithms on poverty prediction tasks and broader multi-view network tasks.
A new triad decoder improves graph auto-encoders' performance.
problem Graph auto-encoders ignore edge interactions, leading to suboptimal predictions.
method Integrates triadic closure property to predict three edges in a local triad.
result Triad decoder leads to more accurate predictions, clustering, and graph characteristics preservation.
Graph attention auto-encoder reconstructs graph structure and attributes.
problem Lack of methods to reconstruct graph structure and node attributes in graph auto-encoders.
method Stacked encoder/decoder layers with self-attention mechanisms, regularized node representations to reconstruct graph structure.
result Competitive performance on node classification benchmarks, including inductive learning.
SF-GCN improves semi-supervised classification by fusing multi-view data structures.
problem Semi-supervised classification challenges due to multi-view data diversity and complexity.
method Structure fusion based on graph convolutional networks (SF-GCN) that balances specificity and commonality.
result SF-GCN outperforms state-of-the-art methods on citation networks datasets.
A new framework learns shared features from multi-view data with many-to-many associations.
problem Learning shared features from multi-view data with many-to-many associations.
method Probabilistic Multi-view Graph Embedding (PMvGE) using neural networks.
result PMvGE outperforms existing multi-view methods in large-scale datasets.
New model for clustering graphs with multiple data sources.
problem Graph clustering with multiple data sources.
method Formalized multi-view stochastic block models and developed efficient algorithms.
result Provable improvement over previous approaches in multi-view graph clustering.
FCMSC combines multi-view data through feature concatenation for improved clustering.
problem Clustering multi-view data with diverse and sometimes incompatible views.
method FCMSC concatenates multi-view data, integrates l2,1-norm, and uses graph regularization to explore consensus and complementary information. result FCMSC outperforms state-of-the-art multi-view clustering methods on six real-world datasets.
LineMVGNN improves AML detection by integrating multi-view graph learning.
problem Ineffective and scalable AML systems using rule-based methods.
method LineMVGNN combines multi-view graph neural networks with line-graph features.
result LineMVGNN outperforms state-of-the-art methods in detecting money laundering.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
SIG-VAE enhances VGAE for graph data modeling.
problem Limited flexibility in VGAE for graph data.
method Hierarchical variational framework with Bernoulli-Poisson link decoder.
result SIG-VAE outperforms state-of-the-art methods on graph tasks.
SpecRaGE learns robust multi-view representations using graph Laplacians and neural networks.
problem Challenges in generalizing and scaling multi-view representation learning methods.
method SpecRaGE integrates graph Laplacian methods with neural networks to learn robust representations.
result SpecRaGE outperforms state-of-the-art methods in noisy and contaminated data scenarios.
Adaptive graph auto-encoder improves general data clustering.
problem Extending graph convolution networks to general clustering tasks.
method Adaptive graph construction based on generative perspective, novel decoder design.
result Model performs well in weighted graph scenarios.
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model us…
Graph-based multi-view model predicts trading volume movement from various sources.
problem Lack of comprehensive understanding of trading volume movement from different sources.
method Graph-based approach incorporating long-term, short-term, and sudden event information.
result Our method outperforms strong baselines by a large margin.
Graph auto-encoder predicts unobserved node features from biological networks and omics data.
problem Integrating biological networks and continuous node features for better prediction.
method Graph neural networks and feature auto-encoders trained on feature reconstruction.
result Graph feature auto-encoder outperforms auto-encoders trained on graph reconstruction for predicting unobserved node features.
A new multi-view clustering method that is fast, scalable, and easy to use.
problem High computational complexity, one-stage fusion, and dataset-specific hyperparameter tuning in multi-view clustering.
method Random view groups, hybrid early-late fusion, diversified base clusterings, and unified bipartite graph.
result Almost linear time and space complexity, no dataset-specific tuning required.
Paper speeds up and extends Subclass Discriminant Analysis methods.
problem Improving efficiency and handling multi-view data in Subclass Discriminant Analysis.
method Developed a speed-up approach based on graph embedding and spectral regression, and a novel multi-view solution.
result Proposed methods achieve competitive performance and significantly decrease training time.
Error-robust multi-view clustering tackles noisy data across multiple sources.
problem Error in multi-view data degrades clustering performance.
method Blind clustering without error consideration is ineffective. Various approaches like sparsity, graph, subspace, and deep learning are reviewed.
result Error-robust multi-view clustering improves clustering accuracy even with corrupted data.
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intu…
A novel multi-layer architecture for one-class classification using graph-embedded kernel ridge regression.
problem Outlier detection in one-class classification using only normal samples.
method Stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
result The proposed method outperforms existing one-class classifiers on 21 benchmark datasets.
Graph auto-encoders predict stock market instability by measuring graph structure changes.
problem Forecasting stock market instability and volatility.
method Use graph auto-encoders to reconstruct graph structure and measure changes.
result Higher GAE reconstruction error correlates with higher volatility.
New method uses graph generative models for graph classification.
problem Graph classification for non-relational i.i.d. data.
method Derive classification formulas from GGM, train generative graph auto-encoder model.
result New conditional ELBO for training graph auto-encoder model.
MV-GNN improves molecular property prediction by integrating atom and bond information.
problem Accurately predicting molecular properties using graph neural networks.
method Multi-View Graph Neural Network (MV-GNN) architecture with shared self-attentive readout and cross-dependent message passing.
result MV-GNN achieves superior performance on molecular property prediction benchmarks.
LMVSC clusters large datasets in linear time, overcoming scalability issues.
problem Efficiency in clustering large-scale multi-view data.
method Learn smaller graphs for each view, integrate them, and apply spectral clustering.
result LMVSC achieves linear complexity for clustering large datasets.
Develops a feature selection method for multi-view data with mixed types.
problem Challenges in feature selection for high-dimensional multi-view data with mixed data types.
method Block Randomized Adaptive Iterative Lasso (B-RAIL) combining randomized Lasso, adaptive weighting, and stability selection.
result Demonstrates effectiveness of B-RAIL in identifying biomarkers and novel candidates for ovarian cancer.
Graph normalizing flows use neural networks for graph prediction and generation.
problem Efficiently processing and generating graph data with reduced memory usage.
method Reversible graph neural network model combining auto-encoder and normalizing flows.
result Graph normalizing flows achieve competitive results in graph generation and prediction.
EGAE improves graph clustering by utilizing GAE's representations in a way consistent with relaxed k-means theory.
problem Improving graph clustering performance using unsupervised methods.
method Designing an Embedding Graph Auto-Encoder (EGAE) that aligns with theoretical relaxed k-means to learn explainable representations.
result EGAE achieves superior graph clustering results compared to existing methods.
A new approach to learn weights for multi-view clustering.
problem Measuring the importance of each view in multi-view clustering.
method Proposes a re-weighted approach to learn intrinsic weights for multi-view clustering.
result The proposed approach is effective and practical for multi-view clustering.
Proposes C2AF network for multi-view time series classification.
problem Improving multi-view time series classification performance.
method Two-stream structured encoder, graph-based correlation matrix, channel-aware fusion mechanism.
result Extensive experimental results show superior performance over state-of-the-art methods.
New methods discover causal relationships from multiple related data views.
problem Causal discovery from non-Gaussian data.
method Multi-view linear Structural Equation Model (SEM) with weak assumptions.
result Identifiability of acyclic SEMs and successful causal graph estimation.
Novel algorithm estimates local permutations in unlabeled multi-view sensing.
problem Estimating local permutations in unlabeled multi-view sensing.
method Graph alignment and Gromov-Wasserstein alignment exploiting multiple views.
result The proposed algorithm is scalable and applicable to challenging SNR regimes.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
GMBL uses graph embedding to learn binary codes from multiple views for clustering.
problem Lack of complete structure and complementary information from multiple views in single-view hash clustering methods.
method Graph-based Multi-view Binary Learning (GMBL) using Laplacian matrix to preserve data structure and assign weights to views.
result GMBL outperforms previous methods in clustering performance on multiple datasets.
LCRSR recovers latent row space for multi-view clustering.
problem Efficiently recover latent representation from multiple views.
method LCRSR assumes latent representation from multiple views, recovers row space, and determines subspace membership.
result LCRSR recovers complete subspace structure efficiently.