Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-ra…
Graph ClassificationGraph Neural NetworkGraph RegressionNode ClassificationGNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
Graph Neural Networks (GNNs) achieve outstanding performance across graph-based tasks but remain difficult to interpret. In this paper, we revisit foundational assumptions underlying model-level explanation methods for G…
ClassificationcounterfactualDecision MakingDiversity+4Learning Long Range Dependencies on Graphs via Random Walks
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often …
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+3GEFL: Extended Filtration Learning for Graph Classification
Extended persistence is a technique from topological data analysis to obtain global multiscale topological information from a graph. This includes information about connected components and cycles that are captured by th…
ClassificationGraph ClassificationGraph Representation LearningRepresentation Learning+1Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving generalization. In this paper, we focus o…
Graph ClassificationGraph Neural NetworkTowards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (and machine learning more generally) to so…
Combinatorial OptimizationGraph ClassificationLink PredictionNode Classification+1Spatio-Spectral Graph Neural Networks
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop n…
GPUGraph ClassificationGraph Neural NetworkGraph Regression+2CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge
Graph Neural Networks (GNNs) are computationally demanding and inefficient when applied to graph classification tasks in resource-constrained edge scenarios due to their inherent process, involving multiple rounds of for…
Computational EfficiencyGraph ClassificationA Canonicalization Perspective on Invariant and Equivariant Learning
In many applications, we desire neural networks to exhibit invariance or equivariance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for atta…
Graph ClassificationGraph EmbeddingGraph RegressionCross-Context Backdoor Attacks against Graph Prompt Learning
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning. While GPL offers superior effective…
Backdoor AttackComputational EfficiencyGraph ClassificationGraph Learning+2Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference cha…
Computational EfficiencyGraph ClassificationGraph ClusteringGraph Learning+4HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph da…
DecoderGraph ClassificationGraph Representation LearningNode Classification+1Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each…
Anomaly DetectionData AugmentationGraph ClassificationGraph Neural Network+3ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from the node representations. In this paper, …
Graph ClassificationPerception-Inspired Graph Convolution for Music Understanding Tasks
We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensio…
Graph ClassificationGraph LearningLink PredictionNode Classification+1Towards Subgraph Isomorphism Counting with Graph Kernels
Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution. Utilizing representation learning has been shown as a promising direction to represent substructures and …
Graph ClassificationRepresentation LearningHypergraph-enhanced Dual Semi-supervised Graph Classification
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising cap…
ClassificationGraph ClassificationTransfer LearningImbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and i…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+1Conditional Local Feature Encoding for Graph Neural Networks
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its l…
Graph ClassificationGraph RegressionLink PredictionNode ClassificationDynamic GNNs for Precise Seizure Detection and Classification from EEG Data
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic pr…
EEGGraph ClassificationGraph LearningGraph Neural Network+2