Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
SA-GDA: Spectral Augmentation for Graph Domain Adaptation
Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. However, most GNNs are primarily studied under the cases of signal domain with supervised training, which requires abundant task-…
Domain AdaptationGRAPH DOMAIN ADAPTATIONNode ClassificationTransfer LearningGraph Triple Attention Network: A Decoupled Perspective
Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, these methods face two primary challenges…
AttributeGraph ClassificationNode ClassificationRW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling
Node classification using Graph Neural Networks (GNNs) has been widely applied in various practical scenarios, such as predicting user interests and detecting communities in social networks. However, recent studies have …
Anomaly DetectionClassificationNode ClassificationDyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification). Inspired by the success of st…
Dynamic Link PredictionDynamic Node ClassificationGraph LearningLanguage Modeling+5Joint Graph Rewiring and Feature Denoising via Spectral Resonance
When learning from graph data, the graph and the node features both give noisy information about the node labels. In this paper we propose an algorithm to jointly denoise the features and rewire the graph (JDR), which im…
DenoisingGraph LearningNode ClassificationPath-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation
Unified graph representation learning aims to produce node embeddings, which can be applied to multiple downstream applications. However, existing studies based on graph neural networks and language models either suffer …
Graph LearningGraph Representation LearningLanguage ModelingLanguage Modelling+4Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative representations. These negative samples inevitabl…
Contrastive LearningNode ClassificationNode ClusteringRepresentation Learning+1Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning
Textual graphs are ubiquitous in real-world applications, featuring rich text information with complex relationships, which enables advanced research across various fields. Textual graph representation learning aims to g…
Graph LearningGraph Representation LearningLanguage ModellingLink Prediction+2wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech
Knowledge graphs (KGs) enhance the performance of large language models (LLMs) and search engines by providing structured, interconnected data that improves reasoning and context-awareness. However, KGs only focus on tex…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderKnowledge Graphs+5Knowledge Probing for Graph Representation Learning
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representa…
Graph ClassificationGraph LearningGraph Representation LearningKnowledge Probing+3Deep Generative Models for Subgraph Prediction
Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queri…
Bayesian OptimizationGraph LearningLink PredictionNode Classification+2Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification
Graph Neural Networks (GNNs) have attracted substantial interest due to their exceptional performance on graph-based data. However, their robustness, especially on heterogeneous graphs, remains underexplored, particularl…
Node ClassificationDerivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence
This paper provides a comprehensive and detailed derivation of the backpropagation algorithm for graph convolutional neural networks using matrix calculus. The derivation is extended to include arbitrary element-wise act…
Explainable artificial intelligenceLink PredictionNode ClassificationContrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a gra…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation LearningA Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offers several opportunities for understanding complex genetic relationships, especially at the RNA level. We present a compre…
Knowledge GraphsNode ClassificationrLLM: Relational Table Learning with LLMs
We introduce rLLM (relationLLM), a PyTorch library designed for Relational Table Learning (RTL) with Large Language Models (LLMs). The core idea is to decompose state-of-the-art Graph Neural Networks, LLMs, and Table Neu…
ClassificationNode ClassificationSharp Bounds for Poly-GNNs and the Effect of Graph Noise
We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node classification. We analyze poly-GNNs under a general contextual sto…
Node ClassificationStochastic Block ModelUniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressive capacity. Many upsampling techniques …
Data AugmentationNode ClassificationGraph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks
Graph data in real-world scenarios undergo rapid and frequent changes, making it challenging for existing graph models to effectively handle the continuous influx of new data and accommodate data withdrawal requests. The…
MemorizationNode ClassificationAutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content…
Graph ClassificationKnowledge GraphsLink PredictionNode Classification