Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
GraphVec: 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 ClassificationGraphNNK -- Graph Classification and Interpretability
Graph Neural Networks (GNNs) have become a standard approach for learning from graph-structured data. However, their reliance on parametric classifiers (most often linear softmax layers) limits interpretability and somet…
Graph ClassificationCommunication-efficient Federated Graph Classification via Generative Diffusion Modeling
Graph Neural Networks (GNNs) unlock new ways of learning from graph-structured data, proving highly effective in capturing complex relationships and patterns. Federated GNNs (FGNNs) have emerged as a prominent distribute…
Graph ClassificationRAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
Graph Foundation Models (GFMs) have emerged as a frontier in graph learning, which are expected to deliver transferable representations across diverse tasks. However, GFMs remain constrained by in-memory bottlenecks: the…
Graph ClassificationGraph LearningBadImplant: Injection-based Multi-Targeted Graph Backdoor Attack
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack for graph classific…
Graph ClassificationGraph Neural NetworkParallelizing Node-Level Explainability in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated remarkable performance in a wide range of tasks, such as node classification, link prediction, and graph classification, by exploiting the structural information in graph-st…
Graph ClassificationNode Classificationgraph partitioningLink PredictionLearning from Historical Activations in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A crucial component of GNNs is the pooling procedure, in which the node feature…
Graph ClassificationFrequent subgraph-based persistent homology for graph classification
Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. Howeve…
Graph Representation LearningGraph ClassificationAL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge. Existing methods particularly those base…
Graph ClassificationGraph LearningFeature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study
The ability to discriminate between generative graph models is critical to understanding complex structural patterns in both synthetic graphs and the real-world structures that they emulate. While Graph Neural Networks (…
Graph ClassificationSharpness-aware Federated Graph Learning
One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from different organizations, raising privac…
Graph ClassificationGraph LearningBeyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-world networks for various applications. G…
Graph ClassificationGraph GenerationParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global…
Graph Representation LearningGraph ClassificationNode ClassificationGraph LearningCORE: Contrastive Masked Feature Reconstruction on Graphs
In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generativ…
Self-Supervised LearningGraph ClassificationContrastive LearningNode ClassificationLightTopoGAT: Enhancing Graph Attention Networks with Topological Features for Efficient Graph Classification
Graph Neural Networks have demonstrated significant success in graph classification tasks, yet they often require substantial computational resources and struggle to capture global graph properties effectively. We introd…
Graph Representation LearningGraph ClassificationGraph Neural NetworkHigh-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on sparsity or Tucker structures and often…
Graph ClassificationLGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is li…
Graph ClassificationGraph Neural NetworkText2Graph: Combining Lightweight LLMs and GNNs for Efficient Text Classification in Label-Scarce Scenarios
Large Language Models (LLMs) have become effective zero-shot classifiers, but their high computational requirements and environmental costs limit their practicality for large-scale annotation in high-performance computin…
Graph ClassificationGraph Neural NetworkText ClassificationSentiment AnalysisPR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learning
Capsule Networks (CapsNets) show exceptional graph representation capacity via dynamic routing and vectorized hierarchical representations, but they model the complex geometries of real\-world graphs poorly by fixed\-cur…
Graph Representation LearningGraph ClassificationGraph LearningEdged Weisfeiler-Lehman Algorithm
As a classical approach on graph learning, the propagation-aggregation methodology is widely exploited by many of Graph Neural Networks (GNNs), wherein the representation of a node is updated by aggregating representatio…
Graph ClassificationGraph Learning