Graph model boosting improves structural data recognition.
problem Limited structural variation capture in existing recognition methods.
method Constructs a large number of graph models and trains decision trees using a boosting framework.
result The proposed method outperforms existing methods on IAM graph database datasets.
PathBoost boosts graph-level predictions using path-based features.
problem Graph-level classification and regression challenges.
method Gradient tree boosting method for graph-level prediction.
result PathBoost outperforms graph neural networks and graph kernel approaches in many cases.
New outlier detection method using graph Laplacian spectrum boosts performance.
problem Detecting outliers in large datasets efficiently.
method Boosted outlier detection based on graph Laplacian spectrum.
result Outperforms existing methods on synthetic datasets.
Boosts GNN performance on molecular graphs.
problem Current GNNs struggle with training set and scalability.
method Proposes an auxiliary module to enhance GNNs.
result Improves GNN performance on molecular datasets.
Efficiently learns subgraph patterns and nonlinear models for graph inputs.
problem Learning relevant subgraph patterns and nonlinear models from large combinatorial inputs.
method Jointly learns subgraph patterns and nonlinear models using gradient boosting.
result Can learn nonlinear models through standard gradient boosting.
Propagation-regularization improves GNN performance by infusing extra graph information.
problem The effectiveness of graph Laplacian regularization in GNNs is questioned and improved upon.
method Introducing Propagation-regularization (P-reg) to enhance GNN performance.
result P-reg boosts GNN performance on various tasks across multiple datasets.
Decentralized learning of personalized models and collaboration graphs without central coordination.
problem Training personalized models and collaboration graphs in a decentralized manner without a central coordinator.
method Alternates between training models given the graph and updating the graph given the models, using peer-to-peer exchanges.
result Communication-efficient approach that avoids exchanging personal data, with benefits demonstrated on synthetic and real datasets.
SST framework boosts GNN performance on few-labeled graph data.
problem Performance degradation of GNNs on graphs with few labeled nodes.
method Stabilized Self-Training (SST) framework for GNNs.
result SST methods achieve superior performance, especially on graphs with few labeled nodes.
Boosting theory explains why multi-scale GNNs work.
problem Over-smoothing in graph neural networks.
method Gradient boosting and transductive learning analysis.
result Test error bound decreases with more node aggregations.
Boost GNNs for node classification by incorporating label dependencies.
problem Current GNNs lack expressiveness and fail to capture label dependencies.
method Proposes a collective learning framework combining collective classification and self-supervised learning.
result Consistent, significant improvement in node classification accuracy across various GNNs.
Learning the right graph representation from noisy, multisource data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here the objective is to incorporate the partial information each data source gives us in a way that captures the true underlying relationships.…
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.
A new method boosts graph neural networks by preventing over-smoothing and over-squashing.
problem Graph Neural Networks struggle with long-range signals and over-smoothing/over-squashing.
method Proposes PowerEmbed, a layer-wise normalization technique inspired by spectral graph embedding.
result PowerEmbed prevents over-smoothing and avoids over-squashing, improving performance on heterophilous graphs.
Unified model for multiple healthcare tasks using graph structure.
problem Multiple healthcare tasks with shared graph structure.
method Graph-Convolutional Network (GCN) with multiple variational autoencoders.
result Boosted performance across all tasks, outperforming state-of-the-art.
Policy-GNN optimizes GNN aggregation for diverse node iterations.
problem Optimizing GNN performance by varying aggregation iterations for different nodes.
method Policy-GNN uses a meta-policy framework with deep reinforcement learning to adaptively determine the number of aggregations for each node.
result Policy-GNN significantly outperforms state-of-the-art alternatives on real-world datasets.
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.
Paper proposes G-CRD to improve GNNs by preserving global graph topology.
problem Improving lightweight GNNs for robust performance on large-scale real-world graphs.
method Introduces Graph Contrastive Representation Distillation (G-CRD) using contrastive learning.
result G-CRD consistently boosts GNN performance and robustness, outperforming existing methods.
Boosts causal discovery by dynamically reweighting samples to learn better DAGs.
problem Overfitting to easier-to-fit samples and violating homogeneity assumptions in causal discovery.
method Adaptive sample reweighting via ReScore function to upweight and downweight samples based on fitting quality.
result Consistent and significant boosts in structure learning performance on synthetic and real-world datasets.
Rewiring edges subtly improves graph neural networks' robustness.
problem Vulnerability of graph neural networks to subtle structural perturbations.
method Proposes a graph rewiring operation and uses reinforcement learning to learn attack strategies.
result Demonstrates the effectiveness of the proposed framework on real-world graphs.
Eigen-GNN enhances GNNs by preserving graph structures.
problem Existing shallow GNNs fail to effectively preserve graph structures.
method Integrates eigenspace of graph structures into GNNs as a dimensionality reduction module.
result Eigen-GNN boosts GNNs' ability to preserve graph structures without increasing depth.
CaGAT learns context-aware edge representations for graph data.
problem Ignoring edge representation in GNNs.
method Unified Context-aware Adaptive Graph Attention Network (CaGAT) that learns both node and edge representations.
result CaGAT improves performance on semi-supervised learning tasks.
Augments graph node features to improve GNN performance.
problem Improving graph neural networks' performance on large-scale datasets.
method Iteratively augments node features with gradient-based adversarial perturbations.
result Boosts model performance in node classification, link prediction, and graph classification tasks.
StackSeq2Seq improves route finding on graphs using deep neural networks.
problem Finding the shortest path between graph nodes.
method Dual Encoder Seq2Seq architecture, context vector, homotopy continuation.
result Increased accuracy in learning shortest routes on graphs.
Deep GNNs and self-supervision boost graph learning at scale.
problem Efficiently deploying GNNs at large scale remains challenging.
method Two large-scale GNNs: a deep transductive node classifier and a very deep inductive graph regressor.
result Award-level performance on MAG240M and PCQM4M benchmarks.
ELCO enhances graph generation by adding new nodes and edges.
problem Improving graph-based semi-supervised learning performance.
method ELCO simulates graph signal generation to add new nodes and edges.
result ELCO significantly boosts model performance on various datasets.
A new model for graph clustering using curvature spaces.
problem Graph clustering from a geometric perspective.
method Introducing a heterogeneous curvature space and a contrastive learning approach.
result CONGREGATE model outperforms state-of-the-art competitors.
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.
GATES improves neural architecture search by modeling operations as information transformation.
problem Improving predictor-based neural architecture search efficiency.
method GATES models operations as information transformation, covering both node and edge cell search spaces.
result GATES boosts sample efficiency and improves predictor performance.
Geometric matrix completion learns graph patterns and non-linear diffusion efficiently.
problem Efficiently learn graph patterns and non-linear diffusion from user/item graphs.
method Geometric deep learning on graphs with graph convolutional and recurrent neural networks.
result Outperforms state-of-the-art techniques on synthetic and real datasets.
BooST combines boosting and smooth trees for nonlinear regression.
problem Estimating partial effects in nonlinear models.
method Boosting smooth trees for partial effect estimation.
result BooST provides more interpretable partial effects than tree-based models.
PairNorm prevents node embeddings from becoming too similar in GNNs, improving performance.
problem Oversmoothing in graph neural networks (GNNs) reduces model performance with deeper layers.
method PairNorm is a normalization layer based on the graph convolution operator, preventing embeddings from becoming too similar.
result PairNorm makes deeper GNNs more robust against oversmoothing and boosts performance.
GraphGAN unifies generative and discriminative models for graph representation learning.
problem Learning effective graph representations from graph data.
method GraphGAN uses a minimax game between a generative and a discriminative model.
result GraphGAN outperforms state-of-the-art methods in various graph tasks.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
Paper shows training can improve GCN performance without changing architecture.
problem Training difficulty of GCNs limits their performance.
method Identified and mitigated energy loss during training.
result Significant decrease in training difficulties and notable performance boost.
Graph neural networks struggle with heterophily, but new designs improve their performance.
problem Graph neural networks struggle with heterophily (networks where connected nodes may have different class labels and dissimilar features).
method Ego- and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations.
result The identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively.
Tree-structured boosting connects gradient boosted stumps and full decision trees.
problem Connecting gradient boosted stumps and full decision trees.
method Introducing tree-structured boosting to create a single decision tree.
result Tree-structured boosting produces models equivalent to CART or gradient boosted stumps at the extremes.
Survey of gradient boosting algorithms and their applications.
problem Improving machine learning models through boosting techniques.
method Detailed mathematical frameworks of gradient boosting algorithms.
result Illustration of gradient boosting's role in model optimization and ranking.
Combines PCA and message passing for better graph node embeddings.
problem Challenges of learning long-range dependencies in graph data.
method PCAPass: PCA + message passing for unsupervised node embeddings.
result Empirically shows competitive performance on node classification benchmarks.
Study on surfaces in Heisenberg group with constant mean curvature.
problem Constant mean curvature surfaces in sub-Lorentzian Heisenberg group.
method First-variation formula derivation and isoperimetric candidates classification.
result Characterization and conjecture of isoperimetric maximizers.
Unified framework for adaptive connection sampling in GNNs improves performance and robustness.
problem Over-smoothing and over-fitting in deep GNNs.
method Adaptive connection sampling trained jointly with GNN model parameters.
result Adaptive connection sampling mathematically equivalent to Bayesian GNNs approximation.
Spectral ranking methods are improved against semi-random graph sampling.
problem Improving spectral ranking methods in semi-random graph sampling.
method Investigating entry-wise error of spectral algorithms against a semi-random adversary.
result Asymptotic performance can be recovered by reweighting observed edges.
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.
This research tackles uncertainty in gradient boosting models using ensemble methods.
problem Quantifying uncertainty in gradient boosting models for high-risk applications.
method Probabilistic ensemble-based framework for gradient boosting classification and regression models.
result Ensembles of gradient boosting models detect anomalous inputs but have limited ability to improve total uncertainty.
Enhances graph modeling with hyperbolic geometry and variational inference.
problem Challenges in modeling relational data with complex dependencies.
method Semi-implicit hierarchical variational Bayes with Poincaré embedding and mutual information regularization.
result Improves graph representation quality and flexibility in edge prediction and node classification.
A method to improve gradient boosting models using stacking.
problem Improving the performance of gradient boosting models.
method Proposes a stacking algorithm to learn a meta-model for ensembles of gradient boosting models.
result The proposed approach can be extended to differentiable combination models like neural networks.
DeGNN improves graph neural networks by decomposing graphs.
problem Graph Convolutional Networks (GCNs) suffer from oversmoothing and limited depth.
method Characterized oversmoothing through information theory, proposed DeGNN for automatic graph decomposition.
result DeGNN boosts performance of general GNNs and achieves state-of-the-art results.
Two extensions improve gradient boosting for multiclass problems.
problem Multiclass classification with small models.
method Two extensions to gradient boosting: vector-valued trees and layer-by-layer boosting.
result Extensions lead to faster convergence and more compact ensembles.
Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…