Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an addi…
Graph ClassificationGraph RegressionLink PredictionGraph Out-of-Distribution Generalization with Controllable Data Augmentation
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., training on small graphs and testing on large …
Data AugmentationGraph ClassificationGraph Neural NetworkOut-of-Distribution Generalization+1S-Mixup: Structural Mixup for Graph Neural Networks
Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored. In this paper, we propose a novel mixup augmentat…
ClassificationGraph ClassificationGraph Neural NetworkNode ClassificationWeisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Graph Neural Networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in terms of graph expressivity. Even thou…
Graph ClassificationGraph Property PredictionGraph RegressionImplicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limited adaptability to learn graph metrics, e…
Generalization BoundsGraph ClassificationGraph LearningRAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph…
Graph ClassificationRetrievalEvaluating Link Prediction Explanations for Graph Neural Networks
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundam…
Graph ClassificationLink PredictionPredictionCounterfactual Explanations for Graph Classification Through the Lenses of Density
Counterfactual examples have emerged as an effective approach to produce simple and understandable post-hoc explanations. In the context of graph classification, previous work has focused on generating counterfactual exp…
counterfactualCounterfactual ExplanationGraph ClassificationHypergraph Isomorphism Computation
The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information. In terms of extracting low-order structural information, graph isomorph…
Community DetectionGraph ClassificationProtein DesignHow Curvature Enhance the Adaptation Power of Framelet GCNs
Graph neural network (GNN) has been demonstrated powerful in modeling graph-structured data. However, despite many successful cases of applying GNNs to various graph classification and prediction tasks, whether the graph…
Graph ClassificationGraph Neural NetworkA Survey on Graph Classification and Link Prediction based on GNN
Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and…
Graph ClassificationGraph Neural NetworkLink PredictionNode ClassificationDynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification
The Dissemination Process Classification (DPC) is a popular application of temporal graph classification. The aim of DPC is to classify different spreading patterns of information or pestilence within a community represe…
ClassificationData AugmentationGraph ClassificationAn Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics
Autonomous individuals establish a structural complex system through pairwise connections and interactions. Notably, the evolution reflects the dynamic nature of each complex system since it recodes a series of temporal …
Graph ClassificationInterpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications involving graph classification. However, dense brain graphs pose compu…
Graph ClassificationModeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices
Recent research has shown that alignment between the structure of graph data and the geometry of an embedding space is crucial for learning high-quality representations of the data. The uniform geometry of Euclidean and …
Graph ClassificationOn Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current node drop pooling methods usually keep …
Graph ClassificationRepresentation LearningStructure-Aware Robustness Certificates for Graph Classification
Certifying the robustness of a graph-based machine learning model poses a critical challenge for safety. Current robustness certificates for graph classifiers guarantee output invariance with respect to the total number …
ClassificationGraph ClassificationStructure-Sensitive Graph Dictionary Embedding for Graph Classification
Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to transform input graphs into the embedding sp…
ClassificationGraph ClassificationVariational InferenceGlobally Interpretable Graph Learning via Distribution Matching
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are arising to explain the model behavior. Exis…
Graph ClassificationGraph LearningA Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
Graph coarsening is a technique for solving large-scale graph problems by working on a smaller version of the original graph, and possibly interpolating the results back to the original graph. It has a long history in sc…
Graph Classificationregression