Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Global Counterfactual Explainer for Graph Neural Networks
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN p…
counterfactualCounterfactual ExplanationCounterfactual ReasoningGraph ClassificationHCL: Improving Graph Representation with Hierarchical Contrastive Learning
Contrastive learning has emerged as a powerful tool for graph representation learning. However, most contrastive learning methods learn features of graphs with fixed coarse-grained scale, which might underestimate either…
Contrastive LearningGraph ClassificationGraph Representation LearningNode Classification+2FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph…
Graph ClassificationEfficient Automatic Machine Learning via Design Graphs
Despite the success of automated machine learning (AutoML), which aims to find the best design, including the architecture of deep networks and hyper-parameters, conventional AutoML methods are computationally expensive …
AutoMLGraph ClassificationGraph Neural Networkimage-classification+1Test-Time Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been noticed. To bridge such gap, in this work w…
Graph ClassificationSelf-Supervised LearningMGNNI: Multiscale Graph Neural Networks with Implicit Layers
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses of implicit GNNs: the constrained express…
Graph ClassificationGraph Neural NetworkNode ClassificationBoosting Graph Neural Networks via Adaptive Knowledge Distillation
Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they learn complementary knowledge from the same…
Graph ClassificationGraph MiningKnowledge DistillationModel Compression+1Pooling Strategies for Simplicial Convolutional Networks
The goal of this paper is to introduce pooling strategies for simplicial convolutional neural networks. Inspired by graph pooling methods, we introduce a general formulation for a simplicial pooling layer that performs: …
Graph ClassificationExpander Graph Propagation
Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks is known to be challenging: it often requires computing node features that are mindful of both local interactions in their neighbou…
Graph ClassificationGraph Representation LearningNavigateRepresentation LearningGeodesic Graph Neural Network for Efficient Graph Representation Learning
Graph Neural Networks (GNNs) have recently been applied to graph learning tasks and achieved state-of-the-art (SOTA) results. However, many competitive methods run GNNs multiple times with subgraph extraction and customi…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+2On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporat…
Code ClassificationData AugmentationGraph ClassificationGraph Learning+1Graph Classification via Discriminative Edge Feature Learning
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when aggregating node features, while omitting…
ClassificationGraph ClassificationTree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Understanding generalization and robustness of machine learning models fundamentally relies on assuming an appropriate metric on the data space. Identifying such a metric is particularly challenging for non-Euclidean dat…
Graph ClassificationGraph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns …
Anomaly DetectionGraph Anomaly DetectionGraph ClassificationLink Prediction+2Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification investigation on the training graphs wi…
counterfactualGraph ClassificationA Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks
Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tas…
Graph ClassificationJoint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).…
3D ReconstructionGraph ClassificationSurface ReconstructionExplainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman i…
Graph ClassificationSPGP: Structure Prototype Guided Graph Pooling
While graph neural networks (GNNs) have been successful for node classification tasks and link prediction tasks in graph, learning graph-level representations still remains a challenge. For the graph-level representation…
Graph ClassificationGraph LearningLink PredictionNode ClassificationGraph Contrastive Learning with Cross-view Reconstruction
Among different existing graph self-supervised learning strategies, graph contrastive learning (GCL) has been one of the most prevalent approaches to this problem. Despite the remarkable performance those GCL methods hav…
Contrastive LearningDisentanglementGraph ClassificationGraph Learning+1