Agent learns to solve unseen tasks with subtask dependencies.
problem Generalizing to unseen tasks with subtask dependencies.
method Neural subtask graph solver (NSGS) with graph reward propagation for pre-training and actor-critic finetuning.
result Agent can find near-optimal subtask execution and generalize to unseen subtask graphs.
Adversarial CBO optimizes under interventions by adversaries and non-stationarities.
problem Optimizing in the presence of adversaries and non-stationary factors.
method Formalizes CBO as ACBO, introduces CBO-MW algorithm combining online learning and causal modeling.
result First algorithm with bounded regret for ACBO, achieving superior performance in synthetic and real-world environments.
Generalized belief propagation converges to optimal solutions on graphs with motifs.
problem Understanding belief propagation on loopy graphs.
method Study of generalized belief propagation on graphs with motifs.
result Generalized belief propagation converges to the global optimum of the Bethe free energy.
Study reveals decurve flows in graph propagation models.
problem Limitations of traditional graph analysis and propagation mechanisms.
method Introduces Generalized Propagation Neural Networks (GPNNs) and Continuous Unified Ricci Curvature (CURC).
result Observation of decurve flow during training of graph neural networks, revealing propagation dynamics.
Improved error correction using neural networks and belief propagation.
problem Inference in factor graphs with loops or poor approximations.
method Hybrid model combining FG-GNN and belief propagation.
result Hybrid model outperforms belief propagation in error correction tasks.
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capt…
Unified model combines feature and label propagation for semi-supervised classification.
problem Combining feature and label propagation for effective semi-supervised classification.
method Unified Message Passing Model (UniMP) using Graph Transformer and masked label prediction.
result Obtains new state-of-the-art results in Open Graph Benchmark (OGB).
New α \alpha α -BP algorithm improves belief propagation for graphs with loops.
problem Uncertainty in belief propagation for graphs with loops.
method Derive α \alpha α -BP algorithm motivated by minimizing α \alpha α -divergence. result Proves convergence conditions for α \alpha α -BP. Proposes MCBO for causal Bayesian optimization with model learning and regret bounds.
problem Maximizing downstream variables in unknown structural models.
method Model-based causal Bayesian optimization (MCBO) that learns full system models and trades off exploration and exploitation.
result First non-asymptotic bounds for CBO and practical implementation showing superior performance.
Improved BP algorithm outperforms loopy BP in MAP inference.
problem Limited understanding and poor performance of belief propagation in graphs with loops.
method Introduced α α α belief propagation, a minimization of localized α α α -divergence. result Significantly outperforms loopy BP in fully-connected graphs for MAP inference.
EBU improves deep RL with episodic backward updates, achieving human-level performance with less data.
problem Efficiently learn from sparse and delayed rewards in reinforcement learning.
method Episodic Backward Update (EBU) algorithm that recursively propagates value updates.
result EBU achieves human-level performance in Atari games with significantly fewer samples.
TIP model improves POSE prediction with less resources.
problem Predicting polypharmacy side effects from drug-protein interactions.
method TIP model operates on three subgraphs for progressive representation learning.
result Improves accuracy by 7%+, time efficiency by 83 i m e s imes im es , and space efficiency by 3 i m e s imes im es . Proposes BSF to evaluate structure learning algorithms without bias.
problem Evaluation bias in structure learning algorithms.
method Balanced Scoring Function (BSF) to adjust reward based on edge difficulty.
result Eliminates bias in favour of underfitted graphs.
Improved text summarization using belief propagation on weighted bipartite graphs.
problem Text summarization from a graph theory perspective.
method Generalized belief propagation algorithm for weighted bipartite graphs.
result Our algorithm outperforms greedy methods in text summarization tasks.
Wave network efficiently propagates long-range information in undirected graphs.
problem Efficiently propagating long-range information in undirected graphs.
method Propagates information in waves of nonlinear computation.
result Wave outperforms graph convolution on three graph-based tasks.
Propagates soft labels on hypergraphs using optimal transportation.
problem Semi-supervised learning on hypergraphs.
method Wasserstein barycenters and message-passing algorithm.
result Generalization error bounds for 2-Wasserstein distance.
LNPE enhances local connections in embeddings using extended neighbor propagation.
problem Improving local connections and interactions in nonlinear dimensionality reduction.
method Inspired by GCN, LNPE extends 1-hop neighbors to n-hop neighbors in LLE.
result LNPE produces more faithful and robust embeddings with better topological and geometrical properties.
Decoupled GCN is shown to be equivalent to label propagation.
problem Improving semi-supervised node classification in graph learning.
method The paper proves the equivalence of decoupled GCN and label propagation, and proposes a new method named PTA.
result Decoupled GCN is equivalent to two-step label propagation and can automatically assign weights to pseudo-labels.
Improved graph classification model using personalized PageRank.
problem Limited neighborhood size in graph neural networks for node classification.
method Derive an improved propagation scheme based on personalized PageRank.
result PPNP and APPNP models outperform previous methods in semi-supervised classification.
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we …
FlowGN tackles graph representation learning by tracing information flow paths.
problem GCNs struggle with over-smoothing and scalability issues.
method FlowGN introduces a 'Source o o o Sink' mode and 'information flow path' concept. result FlowGN outperforms state-of-the-art GCNs in public datasets.
Efficiently learns deep factor graphs using Gaussian belief propagation.
problem Learning in deep factor graphs with efficient inference.
method Treats all relevant quantities as random variables, uses belief propagation for inference.
result Efficiently solves training and prediction problems in deep factor graphs with belief propagation.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
New algorithm for causal bandits with propagating interventions.
problem Handling causal graphs with side-information for sequential decision-making.
method Proposes a novel causal bandit algorithm that can propagate interventions throughout a causal graph.
result Achieves O ( γ ∗ log ( ∣ A ∣ T ) / T ) O(\sqrt{ γ^* \log(|\mathcal{A}|T) / T}) O ( γ ∗ log ( ∣ A ∣ T ) / T ) regret bound, where γ ∗ γ^* γ ∗ depends on causal graph structure. 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.
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.
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such marginals when the underlying graph is a tree. But it has gained its popularity as …
New method uses GNNs and node feature propagation for active learning in graph node classification.
problem Lack of labeled data for graph neural networks.
method Node feature propagation followed by K-Medoids clustering for instance selection.
result Proposed method significantly outperforms other methods on benchmark datasets.
FSD-CAP improves graph feature imputation under high missing rates.
problem Challenges in imputing missing node features in graphs, especially under high missing rates.
method Two-stage framework: subgraph expansion, fractional diffusion, class-aware propagation.
result Significantly improved imputation quality compared to existing methods, achieving high accuracy on benchmark datasets.
Unified model combines GCN and LPA for better node classification.
problem Combining GCN and LPA for improved node classification.
method Unified model that unifies GCN and LPA, learns edge weights and attention weights.
result Unified model outperforms state-of-the-art GCN-based methods in node classification accuracy.
SA-GFN corrects biases in GFlowNets due to graph symmetries.
problem Systematic biases in state transition probability computations.
method Incorporates symmetry corrections into the learning process through reward scaling.
result Eliminates need for explicit state transition computations.
Simplified interactive image segmentation using kNN graphs.
problem Interactive image segmentation with user-provided labels.
method Undirected kNN graphs for label propagation.
result Effective interactive segmentation with significant accuracy.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
PGMax automates PGM inference on GPUs, improving quality and speed.
problem Efficient inference in complex discrete PGMs.
method Factor graph specification and loopy belief propagation in JAX.
result Higher-quality inference with up to 3x speedups.
New algorithm uses GNNs to optimize rewards in graph-structured data.
problem Optimizing rewards in molecule design with graph-structured data.
method Embedding permutation invariance into GNNs and using GNTK for regret bounds.
result First GNN confidence bound and phased-elimination algorithm with sublinear regret.
GHNet improves graph learning by balancing homogeneity and heterogeneity.
problem Over-smoothing in GCN leads to similar node representations.
method GHNet uses gating units to balance homogeneity and heterogeneity in feature propagation.
result GHNet achieves larger receptive fields without over-smoothing.
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.
Agents cooperate to make decisions in multi-armed bandits over a graph.
problem Optimizing decisions in multi-agent multi-armed bandits with shared information.
method Designs consensus-based distributed estimation and cooperative algorithms for group decision-making.
result Achieves group performance close to centralized fusion center.
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
problem Automatic clustering of nodes in data and graph spaces.
method Density-based node clustering with propagation process and spectral clustering on subclusters.
result DPSM effectively clusters nodes in both data and graph spaces.
Optimal algorithms identified for semi-supervised classification on graphs.
problem Clustering and classification on graphs with relational and feature information.
method Bayesian inference and belief propagation, extended to graph convolution neural networks.
result Identification of a phase transition and asymptotically optimal algorithms.
Proposes a method to quantify uncertainty in graph neural networks for node classification.
problem Uncertainty in graph neural networks for node classification.
method Bayesian uncertainty propagation (BUP) method embedding GNNs in a Bayesian framework.
result Demonstrates superior performance of the proposed method on benchmark datasets.
Belief propagation quickly converges to global optima for ferromagnetic Ising models.
problem Understanding convergence of belief propagation on graphs with cycles.
method Natural initialization and analysis of Ising models on arbitrary graphs.
result Belief propagation converges quickly to the global optimum of the Bethe free energy for ferromagnetic Ising models.
TPN learns to propagate labels for few-shot learning.
problem Few-shot learning with limited training instances per class.
method Transductive Propagation Network (TPN) learns to propagate labels from labeled to unlabeled instances.
result TPN achieves state-of-the-art results on multiple benchmark datasets.
Factor graphs are important models for succinctly representing probability distributions in machine learning, coding theory, and statistical physics. Several computational problems, such as computing marginals and partition functions, arise naturally when working with factor graphs. Belief propagation is a widely deplo…
Gaussian BP algorithm converges exponentially under walk summability for cyclic graphs.
problem Convergence rate of Gaussian BP for cyclic graphs.
method Extending known results on walk summability, proving exponential convergence rate.
result Gaussian BP converges exponentially under walk summability for cyclic graphs.
GraphShield uses dynamic graph learning to detect and visualize financial risks.
problem Detecting and mitigating risks in financial networks.
method Enhanced Cross-Domain Information Learning, Advanced Risk Recognition, Risk Propagation Visualization.
result GraphShield effectively identifies and visualizes hidden financial risks.
Meta-learning improves few-shot learning by propagating knowledge across related classes on a graph.
problem Few-shot learning suffers from insufficient training data.
method Developed a Gated Propagation Network (GPN) that learns to propagate messages between prototypes of different classes on a graph.
result GPN outperforms recent meta-learning methods on benchmark datasets.
A new framework SIMBA improves graph classification performance on size-imbalanced datasets.
problem Size imbalance in graph classification leads to poor model performance.
method Energy-guided structural smoothing between head and tail graphs, re-weighting based on energy propagation.
result SIMBA outperforms existing methods in size-imbalanced graph classification tasks.