Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
HL-Net: Heterophily Learning Network for Scene Graph Generation
Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to acquire context information between obje…
Graph ClassificationGraph GenerationScene Graph ClassificationScene Graph GenerationFastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the majority of existing GCL methods elaborate on…
ClassificationContrastive LearningData AugmentationGraph Classification+2DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced the pooling strategy to reduce the size…
Graph ClassificationGraph LearningLiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning
Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting the…
Graph ClassificationGraph Representation LearningRepresentation LearningReinforced Causal Explainer for Graph Neural Networks
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent technique of highlighting the explanatory s…
Graph ClassificationSoftEdge: Regularizing Graph Classification with Random Soft Edges
Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning. Due to their effectiveness, simple…
ClassificationData AugmentationGraph ClassificationGraph neural networks and attention-based CNN-LSTM for protein classification
This paper focuses on three critical problems on protein classification. Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to understand the properties of enzymes. However, one CAZyme may belong…
ClassificationGraph AttentionGraph ClassificationGraph Learning+1BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. …
AttributeGraph ClassificationLink PredictionNode Classification+1Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation. As an essential component of the architecture, graph pooling is indispensable fo…
Graph ClassificationGraph GenerationMulti-view graph structure learning using subspace merging on Grassmann manifold
Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classifica…
Graph ClassificationGraph structure learningLink PredictionMULTI-VIEW LEARNING+1Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization
In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal representations for video summarization. Most existing video summarization methods extract image-level features with ImageNet pre-trai…
Graph ClassificationRelationRelational ReasoningSupervised Video Summarization+1On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Info…
Contrastive LearningGraph ClassificationGraph Representation LearningRepresentation LearningSemisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive. It frequently induces the trouble of model overfitting and performance degradation for the supervised m…
Graph ClassificationHyperspectral Image Classificationimage-classificationImage ClassificationTwin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism test method, namely Twin-WL, which simult…
ClassificationGraph ClassificationVocal Bursts Intensity PredictionFine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. Current studies use exist…
Graph ClassificationGraph LearningGraph Neural NetworkMolecular Property Prediction+1PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning
In order to advance the state of the art in graph learning algorithms, it is necessary to construct large real-world datasets. While there are many benchmark datasets for homogeneous graphs, only a few of them are availa…
ClassificationDescriptivedomain classificationGraph Classification+2Supervised Contrastive Learning with Structure Inference for Graph Classification
Advanced graph neural networks have shown great potentials in graph classification tasks recently. Different from node classification where node embeddings aggregated from local neighbors can be directly used to learn no…
ClassificationContrastive LearningGraph ClassificationGraph Neural Network+1Incorporating Heterophily into Graph Neural Networks for Graph Classification
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels and dissimilar features. In real-world …
ClassificationGraph ClassificationAn explainability framework for cortical surface-based deep learning
The emergence of explainability methods has enabled a better comprehension of how deep neural networks operate through concepts that are easily understood and implemented by the end user. While most explainability method…
AnatomyDeep LearningGraph ClassificationvalidFlurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning
Complex heterogeneous dynamic networks like knowledge graphs are powerful constructs that can be used in modeling data provenance from computer systems. From a security perspective, these attributed graphs enable causali…
Anomaly DetectionGraph ClassificationGraph Representation LearningKnowledge Graphs+1