GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
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Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131
26 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
HyperGRL places graph nodes on a hypersphere and minimizes Helmholtz free energy with structural binding energy and mean-field repulsive potential, regulated by an adaptive thermostat, to produce discriminative representations.
UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
A new multi-view graph contrastive learning method generates adaptive views via learnable fractional-order diffusion dynamics instead of manual augmentations and outperforms prior baselines.
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
SAOT applies structure-aware optimal transport to capture global inter-node correspondences and uses cross-task distillation to retain prior structural knowledge, yielding accuracy gains of up to 15% on Products-CL in class-incremental settings.
GraspLLM extracts dataset-agnostic structural patterns via motif contrastive learning and aligns contextual subgraphs to LLM tokens, outperforming prior LLM-based methods on TAGs especially in zero-shot settings.
HyRAG improves zero-shot generalization of graph foundation models by indexing and retrieving from tree-structured knowledge in hyperbolic space via multi-granularity retrieval and dual-path fusion.
L2IR uses LLMs to extract latent intents from behaviors and connections, improving graph fraud detection under camouflage via adaptive self-training and serving as a plug-in for GNN detectors with up to 8.27% AUPRC gain.
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
GCD-FGL mitigates neighborhood absorption and global semantic inconsistency in federated generalized category discovery, delivering +4.86 average HRScore gain over baselines on five graph datasets.
SDM-SCR uses LLMs for semantic disentanglement of signal from noise in text-attributed graphs followed by spectral consistency regularization to improve contrastive learning performance.
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.
Message passing trivializes positive sample maximization in GCL via Dirichlet energy smoothing; SPGCL mitigates this by propagating only high-energy features and using low-energy ones for positive sampling.
SPG is a graph foundation model using spectral decomposition via Chebyshev filters and Gromov-Wasserstein prototypes for improved cross-graph transferability.
Contrastive FUSE learns node embeddings from partial pairwise supervision and structural signals alone by optimizing a spectral contrastive objective with a lightweight modularity approximation, yielding competitive performance and runtime gains on citation and co-purchase graphs.
CHoE uses structure-conditioned experts, structure-aware routing with load balancing, and prompt-based semantic fusion to improve few-shot performance on cross-domain heterogeneous graph prompt learning tasks.
GP2F is a dual-branch graph prompting framework that fuses frozen pre-trained knowledge with task-specific adaptation to reduce estimation error and outperform baselines in cross-domain few-shot node and graph classification.
OpenGLT benchmark finds no single GNN architecture dominates graph-level tasks, with subgraph-based models strongest in expressiveness, graph learning and SSL models in robustness, node and pooling models in efficiency, and graph topology partially guiding architecture choice.
DiGGR introduces a self-supervised graph representation learning framework that disentangles latent factors to guide mask modeling and improve representation quality on graph tasks.
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
citing papers explorer
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Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
-
Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
-
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
-
Energy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion
HyperGRL places graph nodes on a hypersphere and minimizes Helmholtz free energy with structural binding energy and mean-field repulsive potential, regulated by an adaptive thermostat, to produce discriminative representations.
-
Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker
UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
-
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
A new multi-view graph contrastive learning method generates adaptive views via learnable fractional-order diffusion dynamics instead of manual augmentations and outperforms prior baselines.
-
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
-
SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
SAOT applies structure-aware optimal transport to capture global inter-node correspondences and uses cross-task distillation to retain prior structural knowledge, yielding accuracy gains of up to 15% on Products-CL in class-incremental settings.
-
GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
GraspLLM extracts dataset-agnostic structural patterns via motif contrastive learning and aligns contextual subgraphs to LLM tokens, outperforming prior LLM-based methods on TAGs especially in zero-shot settings.
-
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
HyRAG improves zero-shot generalization of graph foundation models by indexing and retrieving from tree-structured knowledge in hyperbolic space via multi-granularity retrieval and dual-path fusion.
-
L2IR: Revealing Latent Intent in Graph Fraud Detection
L2IR uses LLMs to extract latent intents from behaviors and connections, improving graph fraud detection under camouflage via adaptive self-training and serving as a plug-in for GNN detectors with up to 8.27% AUPRC gain.
-
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
-
Generalized Category Discovery in Federated Graph Learning
GCD-FGL mitigates neighborhood absorption and global semantic inconsistency in federated generalized category discovery, delivering +4.86 average HRScore gain over baselines on five graph datasets.
-
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning
SDM-SCR uses LLMs for semantic disentanglement of signal from noise in text-attributed graphs followed by spectral consistency regularization to improve contrastive learning performance.
-
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
-
Handling Feature Heterogeneity with Learnable Graph Patches
Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.
-
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
Message passing trivializes positive sample maximization in GCL via Dirichlet energy smoothing; SPGCL mitigates this by propagating only high-energy features and using low-energy ones for positive sampling.
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A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
SPG is a graph foundation model using spectral decomposition via Chebyshev filters and Gromov-Wasserstein prototypes for improved cross-graph transferability.
-
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
Contrastive FUSE learns node embeddings from partial pairwise supervision and structural signals alone by optimizing a spectral contrastive objective with a lightweight modularity approximation, yielding competitive performance and runtime gains on citation and co-purchase graphs.
-
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
CHoE uses structure-conditioned experts, structure-aware routing with load balancing, and prompt-based semantic fusion to improve few-shot performance on cross-domain heterogeneous graph prompt learning tasks.
-
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
GP2F is a dual-branch graph prompting framework that fuses frozen pre-trained knowledge with task-specific adaptation to reduce estimation error and outperform baselines in cross-domain few-shot node and graph classification.
-
OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
OpenGLT benchmark finds no single GNN architecture dominates graph-level tasks, with subgraph-based models strongest in expressiveness, graph learning and SSL models in robustness, node and pooling models in efficiency, and graph topology partially guiding architecture choice.
-
Disentangled Generative Graph Representation Learning
DiGGR introduces a self-supervised graph representation learning framework that disentangles latent factors to guide mask modeling and improve representation quality on graph tasks.
-
Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
-
Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
MPAIACL applies contrastive learning to generate adversarial invariant augmentations that improve GNN generalization under covariate shifts on graphs.
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Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.