Tiered graph autoencoders improve molecular graph representation.
problem Representing and utilizing groups in molecular graphs.
method Adapting tiered graph autoencoders for PyTorch Geometric.
result Molecular graphs have tiered latent representations.
New method simplifies causal inference with tiered background knowledge.
problem Large equivalence classes of DAGs limit causal information.
method Integrates tiered background knowledge to create 'tiered MPDAGs' with simplified structure.
result Tiered MPDAGs are chain graphs with chordal components, simplifying causal effect estimation.
Bayesian optimization finds best hyperparameters for deep learning models.
problem Finding optimal hyperparameters in high-dimensional space.
method Bayesian optimization, Gaussian processes, and adaptive experimentation.
result Ax, BoTorch, and GPyTorch provide a powerful and simple framework for hyperparameter optimization.
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
Paper tackles robust knowledge transfer in parallel RL tasks.
problem Transfer knowledge from low-tier to high-tier tasks in parallel RL without shared dynamics or reward functions.
method Identifies Optimal Value Dominance condition and proposes online learning algorithms for both tasks.
result Achieves constant regret on partial states and near-optimal regret when tasks are dissimilar.
IDVAE combines VAE and GAN without explicit discriminator.
problem Combining VAE and GAN for reconstruction and generation.
method Shared encoder and discriminator with combined decoder and generator.
result IDVAE outperforms state-of-the-art hybrid approaches.
AEGCN uses autoencoder constraints to improve graph node classification.
problem Node classification on graph domains with reduced information loss.
method Autoencoder-constrained graph convolutional network (AEGCN).
result Adding autoencoder constraints significantly improves graph convolutional network performance.
Proposes a symmetric graph autoencoder for unsupervised learning.
problem Graph representation learning without labeled data.
method Symmetric graph convolutional autoencoder with Laplacian sharpening and signed graphs.
result Outperforms state-of-the-art algorithms in clustering, link prediction, and visualization tasks.
Paper presents new algorithms for causal discovery with latent variables and overlapping datasets.
problem Causal discovery with latent variables and overlapping datasets.
method Introduces tiered FCI and tIOD algorithms for constraint-based causal discovery.
result The tIOD algorithm is more efficient and informative than the IOD algorithm.
Adversarial training improves graph autoencoder generalization.
problem Improving graph autoencoder generalization.
method Formulated L2 and L1 adversarial training for graph autoencoders and variational graph autoencoders.
result Adversarial training boosts graph autoencoder and variational graph autoencoder generalization.
The paper proposes Tier Balancing for dynamic fairness in decision-making.
problem Achieving long-term fairness in decision-making processes.
method Causal modeling with DAGs to investigate dynamic fairness.
result Tier Balancing is a more natural approach to achieve long-term fairness, capturing latent causal factors.
TiFL divides clients into tiers to improve federated learning performance.
problem Heterogeneity in resource and data quality impacts FL performance.
method TiFL divides clients into tiers based on training performance and selects clients from the same tier in each training round.
result TiFL achieves faster training performance with comparable or better test accuracy.
FastGAE scales graph AE and VAE to large graphs with millions of nodes.
problem Scalability issues in graph AE and VAE.
method Stochastic subgraph decoding scheme to speed up training.
result Outperforms existing approaches on various real-world graphs.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
Graphon autoencoder generates graphs with arbitrary sizes using Chebyshev filters.
problem Generating graphs with arbitrary sizes and arbitrary structures.
method Induces graphons from observed graphs, uses Chebyshev filters for latent representation, and learns encoder and decoder to minimize Wasserstein distance.
result Graphon autoencoder provides a new paradigm for graph generation with good generalizability and transferability.
Dyn-VGAE learns evolving network structures.
problem Learning dynamic network representations.
method Dynamic joint Variational Graph Autoencoders (Dyn-VGAE).
result Dyn-VGAE captures temporal evolution in dynamic networks.
Simple linear model outperforms GCN in graph AE tasks.
problem Challenging tasks like link prediction and node clustering.
method Replaced GCN with a simple linear model on adjacency matrix.
result Simple model consistently reaches competitive performances.
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated decoders that increase the complexity of training and evaluat…
Graph regularized autoencoder improves anomaly detection performance.
problem Unsupervised anomaly detection in high-dimensional data.
method Developed a graph regularized autoencoder using MST-based distances.
result Outperforms alternative methods on 20 benchmark anomaly detection datasets.
In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective propagation mechanism, our…
A new model learns latent spaces for graph data.
problem Scalability and expressivity limitations in graph generative models.
method Sequential Graph Variational Autoencoder (SGVAE) that learns latent spaces directly from graph data.
result Promising results on a cycle dataset, but need for permutation relaxation.
Develops scalable autoencoder for document networks.
problem Sparse and skewed latent node representations in document relational networks.
method Combines graph Poisson factor analysis with Weibull-based graph inference networks.
result Extracts high-quality hierarchical latent document representations.
PieClam autoencodes graphs into communities, improving graph anomaly detection.
problem Graph anomaly detection and universal graph autoencoding.
method Probabilistic graph model with overlapping inclusive and exclusive communities.
result PieClam is a universal autoencoder that uniformly approximates any graph.
Simpler linear models outperform complex GCN encoders for graph tasks.
problem Complex graph autoencoders and variational autoencoders.
method Replacing GCN encoders with one-hop linear models.
result Simpler linear models achieve competitive performance with fewer operations and parameters.
DefenseVGAE defends graph neural networks against adversarial attacks.
problem Vulnerability of GNNs to adversarial structural perturbations.
method Variational Graph Autoencoder (VGAE) to reconstruct graph structure.
result DefenseVGAE reduces adversarial perturbations and boosts GCN performance.
This work creates a deep autoencoding model to interpret graph parameters.
problem Matching observed graph topologies with generative procedures and parameters is challenging.
method Developed a disentanglement-focused Beta-Variational Autoencoder (Beta-VAE) model.
result The model learns disentangled latent variables that represent graph parameters.
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges…
Improved community detection and link prediction with GAE and VGAE.
problem Jointly improving community detection and link prediction with graph autoencoders.
method Introducing a community-preserving message passing scheme and doping encoders with Louvain-based prior communities.
result Empirical effectiveness of Modularity-Aware GAE and VGAE on various real-world graphs.
EVGAE improves VGAE's latent representation learning by mitigating over-pruning.
problem Over-pruning in VGAE limits latent variable capacity and diversity.
method EVGAE uses epitomic approach with multiple sparse VGAE models (epitomes) to increase active latent units and improve generative ability.
result EVGAE outperforms VGAE in generative ability and link prediction on citation networks.
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the laten…
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link pr…
A deep learning model organizes RNA graphs to reveal folding patterns and properties.
problem Organizing and understanding the complex folding patterns of RNA secondary structures.
method Geometric scattering autoencoder (GSAE) network for learning graph embeddings.
result GSAE accurately reflects bistable RNA structures and can sample new folding trajectories.
GAEs are shown to be implicitly contrastive learners, revealing new design axes.
problem Understanding and improving graph autoencoders.
method Reinterpret GAEs as contrastive learners, identifying asymmetric views.
result Interpreting GAEs as contrastive learners offers clearer model understanding and design guidance.
New protocol identifies impossible edge orientations in causal graphs.
problem Causal-discovery algorithms cannot distinguish edge directions without assumptions.
method Discrete impossibility certificates and oracle queries.
result Upper bound of 1+K expert interactions for DAG recovery. DECAF-GAD improves fairness in autoencoder-based GAD models without sacrificing performance.
problem Fairness in autoencoder-based GAD models for node-level anomaly detection.
method DECAF-GAD uses a structural causal model to disentangle sensitive attributes from learned representations, along with a fairness-guided loss function.
result DECAF-GAD significantly enhances fairness metrics while maintaining anomaly detection performance.
VACA models graph data for causal inference without hidden confounders.
problem Causal inference in observational data with hidden confounders.
method Variational graph autoencoders for structural causal models.
result Accurately approximates interventional and counterfactual distributions.
Graph autoencoders improve node embeddings using random walk regularization.
problem Graph autoencoders' reconstruction loss ignores latent representation distribution.
method Random walk regularization to improve latent representations.
result The method achieves state-of-the-art accuracy on link prediction tasks.
Proposes autoencoding with random forests using spectral graph theory.
problem Learning low-dimensional embeddings of random forest models.
method Combines nonparametric statistics and spectral graph theory for optimization.
result Establishes a universal consistent decoder for random forest models.
A novel disentangled graph autoencoder improves treatment effect estimation from networked observational data.
problem Treatment effect estimation from observational data is challenging due to unconfoundedness assumption and latent confounders.
method Proposes a disentangled variational graph autoencoder to disentangle latent factors and enforce factor independence.
result Extensive experiments show superior performance compared to state-of-the-art approaches.
We improve a graph generation model to accurately recover Barabási-Albert graph parameters.
problem Recover Barabási-Albert graph parameters from graph data.
method Use a disentanglement-focused deep autoencoding framework with a sequential LSTM decoder trained on graph data.
result Successfully recover Barabási-Albert graph parameters.
L2G2G improves graph autoencoder accuracy without sacrificing scalability.
problem Scalability issues in graph autoencoders for large networks.
method Dynamic synchronization of latent node representations in each epoch.
result L2G2G achieves higher accuracy than standard Local2Global approach on large and dense networks.
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
problem Improving performance for risk-averse users in reinforcement learning.
method Introducing a tiered reinforcement learning approach with two policies: πextO and πextE. result Achieving constant regret for risk-averse users, independent of the number of episodes.
Modularity-aware GAE and VGAE improve community detection and link prediction.
problem Improving community detection with GAE and VGAE in the absence of node features.
method Introducing a modularity-aware message passing scheme and regularizer to GAE and VGAE encoders.
result Jointly addressing community detection and link prediction with high accuracy is possible.
Improved graph generation model for small organic molecules.
problem Graph generation models struggle with matching training distributions and require expensive graph matching.
method Introduced a message passing neural network into the GVAE's encoder and decoder.
result Demonstrated improved graph generation for small organic molecules.
Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.
problem Optimizing market making in aggregator-routed RFQ markets with varying dealer performance scores.
method Two-tier stochastic control model that separates RFQ-level price competition from macro routing.
result Optimal controls can be expressed through derivatives of reduced Hamiltonians, leading to interpretable mappings from optimal win probabilities to optimal offsets.
Method generates counterfactual explanations for graph classifiers.
problem Generating high-quality explanations for graph predictions.
method Permutation equivariant graph variational autoencoder to traverse latent space.
result Empirically validated model is high-performing and robust.
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.
method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.
result Achieves polynomial-time complexity and accurately recovers ancestral relationships.
We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link…