Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as features of downstream tasks. However, GCL…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation LearningASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network
Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for learning node representations. However, t…
Graph Neural NetworkNode ClassificationIsomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop t…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+3HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs
Recent studies have shown that attackers can catastrophically reduce the performance of GNNs by maliciously modifying the graph structure or node features on the graph. Adversarial training, which has been shown to be on…
Node ClassificationBreaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existing GNNs follow the homophily assumption,…
Graph LearningNode ClassificationNode-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Deep Graph Learning (DGL) has emerged as a crucial technique across various domains. However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility to evasion and poisoning attacks. While empi…
Graph LearningNode ClassificationRecommendation SystemsOn the Initialization of Graph Neural Networks
Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization of model weight matrices within each GN…
Graph ClassificationGraph Representation LearningLink PredictionNode Classification+1Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
A machine learning model is traditionally considered robust if its prediction remains (almost) constant under input perturbations with small norm. However, real-world tasks like molecular property prediction or point clo…
Adversarial RobustnessMolecular Property PredictionNode ClassificationPoint Cloud Segmentation+2The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks
Many Graph Neural Networks (GNNs) add self-loops to a graph to include feature information about a node itself at each layer. However, if the GNN consists of more than one layer, this information can return to its origin…
Node ClassificationTracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
Model Parsing defines the research task of predicting hyperparameters of the generative model (GM), given a generated image as input. Since a diverse set of hyperparameters is jointly employed by the generative model, an…
Node ClassificationUncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational …
Contrastive LearningNode ClassificationPropagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a teacher graph neural network (GNN). Whil…
Graph Neural NetworkKnowledge DistillationNode ClassificationOn the Adversarial Robustness of Graph Contrastive Learning Methods
Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against adversarial attacks. More recently, resea…
Adversarial RobustnessContrastive LearningGraph ClassificationNode ClassificationAttend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training
In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential and discriminative information of structu…
Node ClassificationLarge Language Models as Topological Structure Enhancers for Text-Attributed Graphs
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP). Inspired by the success of LLMs in NLP tasks, some recent work has begun investigating the poten…
Graph LearningGraph Neural NetworkInformation RetrievalNode Classification+5BHGNN-RT: Network embedding for directed heterogeneous graphs
Networks are one of the most valuable data structures for modeling problems in the real world. However, the most recent node embedding strategies have focused on undirected graphs, with limited attention to directed grap…
Graph Neural NetworkNetwork EmbeddingNode ClassificationHard Label Black Box Node Injection Attack on Graph Neural Networks
While graph neural networks have achieved state-of-the-art performances in many real-world tasks including graph classification and node classification, recent works have demonstrated they are also extremely vulnerable t…
ClassificationGraph ClassificationNode ClassificationUnveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN)…
Graph LearningGraph Neural NetworkKnowledge DistillationNode ClassificationNode Classification in Random Trees
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with n…
ClassificationGraph Neural NetworkNode ClassificationSelf-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these methods rely on pairwise relations in the gr…
Link PredictionNode Classification