Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Graph-based Argument Quality Assessment
The paper presents a novel discourse-based approach to argument quality assessment defined as a graph classification task, where the depth of reasoning (argumentation) is evident from the number and type of detected disc…
Graph ClassificationPooling Architecture Search for Graph Classification
Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. GNNs are designed t…
ClassificationGraph ClassificationNeural Architecture SearchGraph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been incre…
Atomic ForcesDeep LearningGraph ClassificationA Hard Label Black-box Adversarial Attack Against Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. …
Adversarial AttackClassificationGraph ClassificationGraph Generation+1Blockchain Phishing Scam Detection via Multi-channel Graph Classification
With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a h…
ClassificationGraph ClassificationGraph EmbeddingGraph Neural Network+1Natural Numerical Networks for Natura 2000 habitats classification by satellite images
Natural numerical networks are introduced as a new classification algorithm based on the numerical solution of nonlinear partial differential equations of forward-backward diffusion type on complete graphs. The proposed …
ClassificationGraph ClassificationGlobal Self-Attention as a Replacement for Graph Convolution
We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework -…
Edge ClassificationGraph ClassificationGraph LearningGraph Property Prediction+5Hyperparameter-free and Explainable Whole Graph Embedding
Graphs can be used to describe complex systems. Recently, whole graph embedding (graph representation learning) can compress a graph into a compact lower-dimension vector while preserving intrinsic properties, earning mu…
Graph ClassificationGraph EmbeddingGraph Representation LearningHyperparameter Optimization+1Compensation Learning
Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert lower weights on samples which are likely to be noisy or quite hard. This study reveals another undis…
BIG-bench Machine LearningGraph Classificationimage-classificationImage Classification+1GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
While graph neural networks (GNNs) have been shown to perform well on graph-based data from a variety of fields, they suffer from a lack of transparency and accountability, which hinders trust and consequently the deploy…
Graph ClassificationNode ClassificationEGC2: Enhanced Graph Classification with Easy Graph Compression
Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial …
ClassificationGraph ClassificationAutomated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link …
Graph ClassificationGraph LearningHyperparameter OptimizationLink Prediction+1Quantum Graph Convolutional Neural Networks
At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantu…
Graph ClassificationDPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
In the big data era, the relationship between entries becomes more and more complex. Many graph (or network) algorithms have already paid attention to dynamic networks, which are more suitable than static ones for fittin…
AttributeDrug DiscoveryFraud DetectionGraph Classification+3On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are a…
ClassificationGraph ClassificationNode ClassificationMaximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
In this paper, we propose a novel pooling layer for graph neural networks based on maximizing the mutual information between the pooled graph and the input graph. Since the maximum mutual information is difficult to comp…
Combinatorial OptimizationGraph ClassificationGraph Neural NetworkInductive BiasEdge Representation Learning with Hypergraphs
Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing informa…
Graph ClassificationGraph ReconstructionGraph Representation LearningRepresentation LearningFederated Graph Classification over Non-IID Graphs
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data…
ClassificationClusteringDynamic Time WarpingFederated Learning+3Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis wi…
Feature CorrelationGraph ClassificationGraph Neural NetworkNode Classification+1Weisfeiler and Lehman Go Cellular: CW Networks
Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems can be attributed to the strong couplin…
Graph ClassificationGraph Property PredictionGraph Regression