Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,024편 · 필터 해제
TopoU-Net: a U-Net architecture for topological domains
Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into grids, graphs, or sequences, obscuring higher-order structure and m…
Graph ClassificationImage ReconstructionNode ClassificationCTQWformer: A CTQW-based Transformer for Graph Classification
Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to capture both global structural dependencies and model the dynamic informatio…
Graph Representation LearningGraph ClassificationGraph LearningHierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
Graphs with heterophily, where adjacent nodes carry different labels, are prevalent in real-world applications, from social networks to molecular interactions. However, existing spectral Graph Neural Network (GNN) approa…
Graph ClassificationGraph Neural NetworkNode ClassificationThe Role of Node Features in Graph Pooling
Graph pooling is commonly applied in graph classification, yet its empirical gains over standard WL-1 expressive GNNs are often marginal or inconsistent. We study this gap by analysing the interaction between node featur…
Graph ClassificationA Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validation tier on three knobs (budget, radii, bandwidth; $\leq 5$ choices ea…
Graph ClassificationA Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification
We introduce PLACE (Persistence-Landmark Analytic Classification Engine), a closed-form pipeline for classifying point clouds and graphs through their persistent-homology signatures. Three quantitative guarantees -- a ma…
Graph ClassificationPoint CloudsMochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as…
Graph ClassificationNode ClassificationLink PredictionEvaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence. In regulated domains, these are crucial for meeting compliance and s…
Graph ClassificationLink PredictionSubgraph Concept Networks: Concept Levels in Graph Classification
The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based explanations, extracted from clusters i…
Graph ClassificationGraph Neural NetworkHow Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. This paper provides a controlled benchmar…
Graph ClassificationGraph LearningTopology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks
Graph neural networks have demonstrated excellent applicability to a wide range of domains, including social networks, biological systems, recommendation systems, and wireless communications. Yet a principled theoretical…
Stochastic OptimizationRecommendation SystemsGraph ClassificationAdversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
Out-of-distribution (OoD) generalization occurs when representation learning encounters a distribution shift. This occurs frequently in practice when training and testing data come from different environments. Covariate …
Representation LearningGraph ClassificationCrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-world settings with shared schemas and f…
parameter-efficient fine-tuningGraph Representation LearningDomain GeneralizationGraph ClassificationReservoir-Based Graph Convolutional Networks
Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this …
Graph ClassificationGraph GenerationInvariant-Stratified Propagation for Expressive Graph Neural Networks
Graph Neural Networks (GNNs) face fundamental limitations in expressivity and capturing structural heterogeneity. Standard message-passing architectures are constrained by the 1-dimensional Weisfeiler-Leman (1-WL) test, …
Graph ClassificationNode ClassificationA Theory of Random Graph Shift in Truncated-Spectrum vRKHS
This paper develops a theory of graph classification under domain shift through a random-graph generative lens, where we consider intra-class graphs sharing the same random graph model (RGM) and the domain shift induced …
Graph ClassificationDomain AdaptationGraph LearningHEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edges
Knowledge Graph(KG) has gained traction as a machine-readable organization of real-world knowledge for analytics using artificial intelligence systems. Graph Neural Network(GNN), is proven to be an effective KG embedding…
Graph ClassificationGraph Neural NetworkNode ClassificationKnowledge GraphsGP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misalignment between pre-training objectives and downstream tasks. Recently, …
Cross-Domain Few-ShotGraph ClassificationMapper-GIN: Lightweight Structural Graph Abstraction for Corrupted 3D Point Cloud Classification
Robust 3D point cloud classification is often pursued by scaling up backbones or relying on specialized data augmentation. We instead ask whether structural abstraction alone can improve robustness, and study a simple to…
3D Point Cloud ClassificationGraph ClassificationData AugmentationGraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even attribute availability. We propose Grap…
Representation LearningCross-Domain Few-ShotGraph ClassificationNode Classification