Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Multitask Active Learning for Graph Anomaly Detection
In the web era, graph machine learning has been widely used on ubiquitous graph-structured data. As a pivotal component for bolstering web security and enhancing the robustness of graph-based applications, the significan…
Active LearningAnomaly DetectionGraph Anomaly DetectionInformativeness+1MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage
Despite remarkable success in diverse web-based applications, Graph Neural Networks(GNNs) inherit and further exacerbate historical discrimination and social stereotypes, which critically hinder their deployments in high…
AttributeFairnessGraph LearningNode ClassificationDeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutio…
Contrastive LearningGraph structure learningNode ClassificationGraph Contrastive Invariant Learning from the Causal Perspective
Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usually believed to learn the invariant repres…
Contrastive LearningNode ClassificationAdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ com…
Federated LearningGraph LearningNode ClassificationInfinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years. However, their performance is often const…
Graph LearningNode ClassificationDistribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and unlabeled nodes. Self-training has emerged…
Node ClassificationDisentangled Condensation for Large-scale Graphs
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the original graph. Despite the promising result…
Graph LearningLink PredictionNode ClassificationRethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage to the spatial domain. As previous studi…
Node ClassificationGraph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The pr…
Generative Adversarial NetworkGraph Representation LearningLayout GenerationNode Classification+1Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks. Cross-network node classification extends GNN techniques to account for domain drif…
Autism detectionClassificationNode ClassificationPredicting the structure of dynamic graphs
Many aspects of graphs have been studied in depth. However, forecasting the structure of a graph at future time steps incorporating unseen, new nodes and edges has not gained much attention. In this paper, we present suc…
Incremental LearningLink PredictionNode ClassificationTime Series+1SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation
Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for…
Graph LearningInterpretable Machine LearningNode ClassificationRecommendation SystemsEffective backdoor attack on graph neural networks in link prediction tasks
Graph Neural Networks (GNNs) are a class of deep learning models capable of processing graph-structured data, and they have demonstrated significant performance in a variety of real-world applications. Recent studies hav…
Backdoor AttackGraph ClassificationLink PredictionNode ClassificationMultimodal weighted graph representation for information extraction from visually rich documents.
This paper introduces a novel system for information extraction from visually rich documents (VRD) using a weighted graph representation. The proposed method aims to improve the performance of the information extraction …
Document Layout Analysisdocument understandingGraph Neural NetworkInformation Retrieval+2Strong Transitivity Relations and Graph Neural Networks
Local neighborhoods play a crucial role in embedding generation in graph-based learning. It is commonly believed that nodes ought to have embeddings that resemble those of their neighbors. In this research, we try to car…
Graph Neural NetworkNode ClassificationA clean-label graph backdoor attack method in node classification task
Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and create a more stealthy backdoor attac…
Backdoor AttackNode ClassificationHierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
We investigate the problem of multiplex graph embedding, that is, graphs in which nodes interact through multiple types of relations (dimensions). In recent years, several methods have been developed to address this prob…
Graph EmbeddingLink PredictionNode ClassificationMitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
This study utilizes community structures to address node degree biases in message-passing (MP) via learnable graph augmentations and novel graph transformers. Recent augmentation-based methods showed that MP neural netwo…
Node ClassificationNode ClusteringSelf-Supervised LearningExplainability-Based Adversarial Attack on Graphs Through Edge Perturbation
Despite the success of graph neural networks (GNNs) in various domains, they exhibit susceptibility to adversarial attacks. Understanding these vulnerabilities is crucial for developing robust and secure applications. In…
Adversarial AttackNode Classification