Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
One for All: Towards Training One Graph Model for All Classification Tasks
Designing a single model to address multiple tasks has been a long-standing objective in artificial intelligence. Recently, large language models have demonstrated exceptional capability in solving different tasks within…
AllGraph ClassificationGraph LearningIn-Context Learning+2Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-based model by predicting the latent repre…
Contrastive LearningData AugmentationGraph ClassificationGraph Regression+1GDM: Dual Mixup for Graph Classification with Limited Supervision
Graph Neural Networks (GNNs) require a large number of labeled graph samples to obtain good performance on the graph classification task. The performance of GNNs degrades significantly as the number of labeled graph samp…
DiversityGraph ClassificationGraph SamplingRecovering Missing Node Features with Local Structure-based Embeddings
Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to recover completely missing node features…
Graph ClassificationGraph Neural Networks Use Graphs When They Shouldn't
Predictions over graphs play a crucial role in various domains, including social networks and medicine. Graph Neural Networks (GNNs) have emerged as the dominant approach for learning on graph data. Although a graph-stru…
Graph ClassificationFiltration Surfaces for Dynamic Graph Classification
Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs). However, current baselines have scalability issues, cannot handle a changing node …
ClassificationGraph ClassificationGeneralized Simplicial Attention Neural Networks
Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to inco…
Graph ClassificationImputationTrajectory PredictionMQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation a…
Graph AttentionGraph ClassificationWhere Did the Gap Go? Reassessing the Long-Range Graph Benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Gr…
Graph ClassificationGraph LearningGraph RegressionHyperparameter Optimization+2Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class dist…
Contrastive LearningGraph ClassificationKnowledge DistillationRepresentation LearningCurvature-based Pooling within Graph Neural Networks
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between nodes making them indistinguishable, over-sq…
Graph ClassificationSelf-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is crucial for disease assessment and treatmen…
Activity PredictionGraph ClassificationGraph Neural NetworkPredictionSpatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN
This study introduced the use of Graph Neural Network (GNN) for predicting the weather and weekday of a day in London, from the dataset of Santander Cycles bike-sharing system as a graph classification task. The proposed…
Graph ClassificationGraph Neural NetworkTransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-based recommender systems involves recur…
Collaborative FilteringGraph ClassificationGraph Representation LearningRecommendation Systems+1Universal Graph Continual Learning
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of graph continual learning such as increme…
Continual LearningGraph ClassificationGraph LearningNode ClassificationCached Operator Reordering: A Unified View for Fast GNN Training
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation po…
Graph AttentionGraph ClassificationNode ClassificationGraph isomorphism UNet
Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar meth…
DecoderGraph ClassificationGraph EmbeddingEnhancing Graph Transformers with Hierarchical Distance Structural Encoding
Graph transformers need strong inductive biases to derive meaningful attention scores. Yet, current methods often fall short in capturing longer ranges, hierarchical structures, or community structures, which are common …
Graph ClassificationGraph RegressionNode ClassificationFunctional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors
This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative proble…
Contrastive LearningEEGEeg DecodingElectroencephalogram (EEG)+1Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link predi…
Graph ClassificationGraph Representation LearningIsomorphism TestingLink Prediction+4