Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing iss…
Graph Neural NetworkHeterogeneous Node ClassificationLink PredictionNode ClassificationIntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
Graph Neural Networks (GNNs) have shown great performance in various tasks, with the core idea of learning from data labels and aggregating messages within the neighborhood of nodes. However, the common challenges in gra…
Data AugmentationNode ClassificationUncertainty for Active Learning on Graphs
Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the highest uncertainty. While it has proven eff…
Active LearningNode ClassificationLying Graph Convolution: Learning to Lie for Node Classification Tasks
In the context of machine learning for graphs, many researchers have empirically observed that Deep Graph Networks (DGNs) perform favourably on node classification tasks when the graph structure is homophilic (\ie adjace…
Node ClassificationTraining-free Graph Neural Networks and the Power of Labels as Features
We propose training-free graph neural networks (TFGNNs), which can be used without training and can also be improved with optional training, for transductive node classification. We first advocate labels as features (LaF…
Node ClassificationGenerating Robust Counterfactual Witnesses for Graph Neural Networks
This paper introduces a new class of explanation structures, called robust counterfactual witnesses (RCWs), to provide robust, both counterfactual and factual explanations for graph neural networks. Given a graph neural …
counterfactualExplanation GenerationGraph Neural NetworkNode ClassificationNoisy Node Classification by Bi-level Optimization based Multi-teacher Distillation
Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillati…
Node ClassificationRepresentation LearningSelf-Supervised LearningAre Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominantly have an …
Graph ClassificationGraph EmbeddingGraph LearningGraph Neural Network+4CKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of c…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationA Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only
Graph Convolutional Networks (GCNs) have shown excellent performance in dealing with various graph structures such as node classification, graph classification and other tasks. However,recent studies have shown that GCNs…
Backdoor AttackGraph ClassificationNode ClassificationMulti-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the c…
Node ClassificationSelf-Supervised LearningGraph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning l…
ClassificationClusteringGraph LearningNode ClassificationYou do not have to train Graph Neural Networks at all on text-attributed graphs
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node classification tasks. Graph Neural Networks …
AllAttributeClassificationNode Classification+1AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer
Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level. However, few existing studies ha…
AttributeNode ClassificationRepresentation LearningRethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis
Recently, the optimization of polynomial filters within Spectral Graph Neural Networks (GNNs) has emerged as a prominent research focus. Existing spectral GNNs mainly emphasize polynomial properties in filter design, int…
Node ClassificationHyperbolic Heterogeneous Graph Attention Networks
Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space. However, because heterogeneous graphs inherently possess comp…
ClusteringGraph AttentionGraph EmbeddingNode ClassificationPolynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently achieving notable success in graph-based…
Anomaly DetectionGraph Anomaly DetectionGraph LearningNode ClassificationHierarchical Attention Models for Multi-Relational Graphs
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively ope…
Graph AttentionLink PredictionNode ClassificationRelationVideoSAGE: Video Summarization with Graph Representation Learning
We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by…
Graph Representation LearningNode ClassificationRepresentation LearningVideo SummarizationFair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. However, challenges arise from biases that c…
counterfactualFairnessGraph Neural NetworkLink Prediction+1