Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Graph convolution networks, like message passing graph convolution networks (MPGCNs), have been a powerful tool in representation learning of networked data. However, when data is heterogeneous, most architectures are li…
Graph ClassificationRepresentation LearningAttacking Graph Classification via Bayesian Optimisation
Graph neural networks have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to at…
Adversarial RobustnessBayesian OptimisationClassificationGraph ClassificationOn the approximation capability of GNNs in node classification/regression tasks
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on graphs defined b…
ClusteringGraph ClassificationNode ClassificationregressionEvaluating Modules in Graph Contrastive Learning
The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semanti…
Contrastive LearningGraph ClassificationGraph Representation LearningRepresentation LearningGraph Domain Adaptation: A Generative View
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tas…
DisentanglementDomain AdaptationGraph ClassificationGRAPH DOMAIN ADAPTATION+2Learnable Hypergraph Laplacian for Hypergraph Learning
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing convolution filters are localized and determ…
Graph ClassificationNode ClassificationLearnable Hypergraph Laplacian for Hypergraph Learning
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. However, most existing convolution filters are localized and determ…
Graph ClassificationNode ClassificationAKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limit…
ClassificationGraph ClassificationGraph LearningGraph Mining+3Do Transformers Really Perform Bad for Graph Representation?
The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level …
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+2Relation order histograms as a network embedding tool
In this work, we introduce a novel graph embedding technique called NERO (Network Embedding based on Relation Order histograms). Its performance is assessed using a number of well-known classification problems and a newl…
GPUGraph ClassificationGraph EmbeddingNetwork Embedding+1Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels
Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks. A popular approach to learningwith graph structured data is to make use of graph…
Graph ClassificationGraph2Graph Learning with Conditional Autoregressive Models
We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers ``simple'' problems such as graph classification or regressing real-valued graph properties. Fo…
Graph ClassificationGraph LearningGraph Neural NetworkConvergent Graph Solvers
We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence. CGS syste…
Graph ClassificationGraph Neural NetworkMixup for Node and Graph Classification
Mixup is an advanced data augmentation method for training neural network based image classifiers, which interpolates both features and labels of a pair of images to produce synthetic samples. However, devising the Mixup…
ClassificationData AugmentationGraph ClassificationGraph Learning+1How Attentive are Graph Attention Networks?
Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors g…
Graph AttentionGraph ClassificationGraph Property PredictionGraph Regression+4Self-Supervised Graph Representation Learning via Topology Transformations
We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide applicability of Graph Convolutional Ne…
Graph ClassificationGraph Representation LearningLink PredictionNode Classification+2Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementation such as network architectures due to…
Computational EfficiencyGraph ClassificationPoint Cloud SegmentationMulti-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
The morphology and distribution of microcalcifications in a cluster are the most important characteristics for radiologists to diagnose breast cancer. However, it is time-consuming and difficult for radiologists to ident…
Graph ClassificationGraph LearningGraph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
Online stores often utilize product relationships such as bundles and substitutes to improve their catalog quality and guide customers through myriad choices. Entity resolution using pairwise product matching models offe…
Entity ResolutionGraph Classificationgraph partitioningStructure-Aware Hierarchical Graph Pooling using Information Bottleneck
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. For these tasks, different pooling strategies have been proposed to generate a graph-level representa…
General ClassificationGraph Classification