Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models still struggle to cap…
Graph ClassificationGraph LearningGraph Neural NetworkNode Classification+1MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization
Electronic Design Automation (EDA) is essential for IC design and has recently benefited from AI-based techniques to improve efficiency. Logic synthesis, a key EDA stage, transforms high-level hardware descriptions into …
Graph ClassificationGraph Representation LearningMulti-Task LearningRepresentation LearningGSpect: Spectral Filtering for Cross-Scale Graph Classification
Identifying structures in common forms the basis for networked systems design and optimization. However, real structures represented by graphs are often of varying sizes, leading to the low accuracy of traditional graph …
ClassificationGraph ClassificationGSTAM: Efficient Graph Distillation with Structural Attention-Matching
Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classification, involve computationally intensive p…
ClassificationGraph ClassificationNode ClassificationLearning Tree-Structured Composition of Data Augmentation
Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transformations sequentially to the data. Existin…
Contrastive LearningData AugmentationGraph ClassificationGraph Classification via Reference Distribution Learning: Theory and Practice
Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neur…
Feature EngineeringGraph ClassificationLearning TheoryGraph Classification with GNNs: Optimisation, Representation and Inductive Bias
Theoretical studies on the representation power of GNNs have been centered around understanding the equivalence of GNNs, using WL-Tests for detecting graph isomorphism. In this paper, we argue that such equivalence ignor…
Graph ClassificationInductive BiasDynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
Persistent homology, a fundamental technique within Topological Data Analysis (TDA), captures structural and shape characteristics of graphs, yet encounters computational difficulties when applied to dynamic directed gra…
Graph ClassificationGraph Neural NetworkTopological Data AnalysisGraph Triple Attention Network: A Decoupled Perspective
Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, these methods face two primary challenges…
AttributeGraph ClassificationNode ClassificationA Structural Feature-Based Approach for Comprehensive Graph Classification
The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity…
ClassificationGraph ClassificationGraph LearningBeyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images
Early detection of dementia, such as Alzheimer's disease (AD) or mild cognitive impairment (MCI), is essential to enable timely intervention and potential treatment. Accurate detection of AD/MCI is challenging due to the…
DiagnosticGraph ClassificationDIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
This paper addresses the challenge of out-of-distribution (OOD) generalization in graph machine learning, a field rapidly advancing yet grappling with the discrepancy between source and target data distributions. Traditi…
Graph ClassificationGraph LearningModel SelectionNatural Language Understanding+1Knowledge Probing for Graph Representation Learning
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representa…
Graph ClassificationGraph LearningGraph Representation LearningKnowledge Probing+3Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
Graph out-of-distribution (OOD) generalization remains a major challenge in graph learning since graph neural networks (GNNs) often suffer from severe performance degradation under distribution shifts. Invariant learning…
Contrastive LearningData AugmentationGraph ClassificationGraph Learning+1AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content…
Graph ClassificationKnowledge GraphsLink PredictionNode ClassificationData Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading t…
Data AugmentationGraph ClassificationEnhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques
The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, nec…
Graph ClassificationGraph Neural NetworkDisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in real-w…
ClassificationGraph ClassificationRepresentation LearningTackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, r…
Graph ClassificationGraph Neural NetworkRelaxing Graph Transformers for Adversarial Attacks
Existing studies have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Even though Graph Transformers (GTs) surpassed Message-Passing GNNs on several benchmarks, their adversarial robustness…
Adversarial RobustnessGraph ClassificationNode Classification