Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Fast Attributed Graph Embedding via Density of States
Given a node-attributed graph, how can we efficiently represent it with few numerical features that expressively reflect its topology and attribute information? We propose A-DOGE, for Attributed DOS-based Graph Embedding…
AttributeGraph ClassificationGraph EmbeddingGraph MiningGraphon based Clustering and Testing of Networks: Algorithms and Theory
Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include classific…
ClassificationClusteringGraph ClassificationGraph Clustering+1Inference Attacks Against Graph Neural Networks
Graph is an important data representation ubiquitously existing in the real world. However, analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph embedding is a powerful tool to sol…
Graph ClassificationGraph EmbeddingGraph ReconstructionReconstruction AttackPermute Me Softly: Learning Soft Permutations for Graph Representations
Graph neural networks (GNNs) have recently emerged as a dominant paradigm for machine learning with graphs. Research on GNNs has mainly focused on the family of message passing neural networks (MPNNs). Similar to the Wei…
Graph ClassificationGraph Neural NetworkGraph RegressionG-Mixup: Graph Augmentation for Graph Classification
This work develops \emph{mixup to graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels of random two samples. Traditionally, Mixu…
ClassificationGraph ClassificationA Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
Distribution shifts, in which the training distribution differs from the testing distribution, can significantly degrade the performance of Graph Neural Networks (GNNs). Although some existing graph classification benchm…
Domain GeneralizationGraph ClassificationOut-of-Distribution GeneralizationWasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data
Defining a valid graph distance is a challenging task in graph machine learning because we need to consider the theoretical validity of the distance, its computational complexity, and effectiveness as a distance between …
Graph ClassificationvalidMetric Learning on Temporal Graphs via Few-Shot Examples
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart. This is of critical importance in many graph classification applicat…
Drug DiscoveryGraph ClassificationMetric LearningRepresentation LearningIntrusion-Free Graph Mixup
We present a simple and yet effective interpolation-based regularization technique to improve the generalization of Graph Neural Networks (GNNs). We leverage the recent advances in Mixup regularizer for vision and text…
Graph ClassificationGeometric Random Walk Graph Neural Networks via Implicit Layers
Graph neural networks have recently attracted a lot of attention and have been applied with great success to several important graph problems. The Random Walk Graph Neural Network model was recently proposed as a more in…
Graph ClassificationGraph Neural NetworkRevisiting Virtual Nodes in Graph Neural Networks for Link Prediction
It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all nodes in the graph. Intuitively, the virtua…
ClusteringGraph ClassificationLink PredictionInductive Lottery Ticket Learning for Graph Neural Networks
Deep graph neural networks (GNNs) have gained increasing popularity, while usually suffer from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplore…
Graph ClassificationNode ClassificationTransductive Learning$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions. Hard negatives can improve the representation ability on the basis of common negatives. Inspired by recent hard…
ClassificationContrastive LearningGraph Classificationimage-classification+2Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mi…
Graph ClassificationNode ClassificationThe Infinite Contextual Graph Markov Model
The Contextual Graph Markov Model is a deep, unsupervised, and probabilistic model for graphs that is trained incrementally on a layer-by-layer basis. As with most Deep Graph Networks, an inherent limitation is the lack …
Graph ClassificationmodelModel SelectionEdge but not Least: Cross-View Graph Pooling
Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been developed to coarsen an input graph into a succi…
Graph ClassificationGraph RegressionGraph Representation LearningRepresentation LearningOrthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations. These models rely on message passing and feature transformation functions to encode the structura…
AttributeGraph ClassificationA Meta-Learning Approach for Training Explainable Graph Neural Networks
In this paper, we investigate the degree of explainability of graph neural networks (GNNs). Existing explainers work by finding global/local subgraphs to explain a prediction, but they are applied after a GNN has already…
Graph ClassificationMeta-LearningNode ClassificationStructural Optimization Makes Graph Classification Simpler and Better
In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the comple…
ClassificationGraph ClassificationAn Empirical Study of Graph Contrastive Learning
Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. Although remarkable progress has been witnessed recently, the success behind GCL is still left som…
Graph ClassificationManagementNode Classification