Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a dominant approach in graph representation learning, yet they often struggle to capture consistent similarity relationships among graphs. While graph kernel methods such as t…
Graph ClassificationGraph Representation LearningRepresentation LearningReducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
In this work, we generalize the ideas of Kaiming initialization to Graph Neural Networks (GNNs) and propose a new scheme (G-Init) that reduces oversmoothing, leading to very good results in node and graph classification …
Graph ClassificationTowards Dynamic Message Passing on Graphs
Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message passing and restricts the ability of GNNs…
Graph ClassificationUncovering Capabilities of Model Pruning in Graph Contrastive Learning
Graph contrastive learning has achieved great success in pre-training graph neural networks without ground-truth labels. Leading graph contrastive learning follows the classical scheme of contrastive learning, forcing mo…
Contrastive LearningGraph ClassificationTransfer LearningTensor-Fused Multi-View Graph Contrastive Learning
Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data. However, current GCL models face c…
Contrastive LearningGraph ClassificationConditional Uncertainty Quantification for Tensorized Topological Neural Networks
Graph Neural Networks (GNNs) have become the de facto standard for analyzing graph-structured data, leveraging message-passing techniques to capture both structural and node feature information. However, recent studies h…
Decision MakingGraph ClassificationNavigatePrediction+2Conditional Prediction ROC Bands for Graph Classification
Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (CP-ROC) bands, offering uncertainty quant…
ClassificationDrug DiscoveryGraph ClassificationPrediction+1FIT-GNN: Faster Inference Time for GNNs Using Coarsening
Scalability of Graph Neural Networks (GNNs) remains a significant challenge, particularly when dealing with large-scale graphs. To tackle this, coarsening-based methods are used to reduce the graph into a smaller graph, …
ClassificationGraph ClassificationGraph RegressionNode Classification+2Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph Classification
We revisit the effectiveness of topological descriptors for molecular graph classification and design a simple, yet strong baseline. We demonstrate that a simple approach to feature engineering - employing histogram aggr…
Feature EngineeringGraph ClassificationExplanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
Graph representation learning (GRL), enhanced by graph augmentation methods, has emerged as an effective technique achieving performance improvements in wide tasks such as node classification and graph classification. In…
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningGraph Classification Gaussian Processes via Hodgelet Spectral Features
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed by transforming spatial features from t…
ClassificationGaussian ProcessesGraph ClassificationGraph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification
This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high com…
Graph ClassificationGraph Neural NetworkTime SeriesTime Series Analysis+1Degree-Conscious Spiking Graph for Cross-Domain Adaptation
Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification by emulating brain-inspired neural dynamics to achieve energy-efficient computation. However, existing SGNs are generally cons…
ClassificationDomain AdaptationGraph ClassificationGRAPH DOMAIN ADAPTATION+1Vector-ICL: In-context Learning with Continuous Vector Representations
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from b…
ClassificationGraph ClassificationIn-Context LearningLanguage Modeling+6Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users. However, these models might be backdoored, and malicious users can su…
Backdoor AttackClusteringGraph ClassificationGraph LearningNeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional…
2-task Classification4-task ClassificationAlzheimer's DetectionFunctional Connectivity+4Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data from diffusion tensor imaging (DTI) and functional …
DiagnosticGraph ClassificationHigher-Order Message Passing for Glycan Representation Learning
Glycans are the most complex biological sequence, with monosaccharides forming extended, non-linear sequences. As post-translational modifications, they modulate protein structure, function, and interactions. Due to thei…
DiversityGraph ClassificationLink PredictionRepresentation LearningPropEnc: A Property Encoder for Graph Neural Networks
Graph machine learning, particularly using graph neural networks, heavily relies on node features. However, many real-world systems, such as social and biological networks, lack node features due to privacy concerns, inc…
Computational EfficiencyGraph ClassificationMDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length
Graph pooling compresses graphs and summarises their topological properties and features in a vectorial representation. It is an essential part of deep graph representation learning and is indispensable in graph-level ta…
Community DetectionGraph ClassificationGraph Representation LearningRepresentation Learning