Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Graph Semi-Supervised Learning for Point Classification on Data Manifolds
We propose a graph semi-supervised learning framework for classification tasks on data manifolds. Motivated by the manifold hypothesis, we model data as points sampled from a low-dimensional manifold $\mathcal{M} \subset…
ClassificationGraph Neural Networkimage-classificationImage Classification+1Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as us…
Data PoisoningGraph LearningGraph Representation LearningNode Classification+2Wasserstein Hypergraph Neural Network
The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learning has become mainstream over the past …
Graph Representation LearningNode ClassificationRepresentation LearningMitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning
Graph Neural Networks (GNNs) often suffer from degree bias in node classification tasks, where prediction performance varies across nodes with different degrees. Several approaches, which adopt Graph Contrastive Learning…
Contrastive LearningNode ClassificationiN2V: Bringing Transductive Node Embeddings to Inductive Graphs
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V are limited in their application on new n…
Node ClassificationRepresentation LearningHGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
Heterogeneous Graph Neural Networks (HGNNs), have demonstrated excellent capabilities in processing heterogeneous information networks. Self-supervised learning on heterogeneous graphs, especially contrastive self-superv…
Graph Neural NetworkNode ClassificationSelf-Supervised LearningWeak Supervision for Real World Graphs
Node classification in real world graphs often suffers from label scarcity and noise, especially in high stakes domains like human trafficking detection and misinformation monitoring. While direct supervision is limited,…
Contrastive LearningMisinformationNode ClassificationRepresentation LearningDeGLIF for Label Noise Robust Node Classification using GNNs
Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out…
DenoisingNode ClassificationImproving the Effective Receptive Field of Message-Passing Neural Networks
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena such as over-squashing, where long-range d…
Graph ClassificationGraph RegressionNode ClassificationGraph Positional Autoencoders as Self-supervised Learners
Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency an…
Graph Property PredictionMissing ElementsNode ClassificationProperty Prediction+2Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
Graph neural networks (GNNs) have shown great ability for node classification on graphs. However, the success of GNNs relies on abundant labeled data, while obtaining high-quality labels is costly and challenging, especi…
Domain AdaptationLink PredictionNode ClassificationUnsupervised Domain AdaptationDirected Homophily-Aware Graph Neural Network
Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic neighborhoods. Additionally, many GNNs ign…
Graph Neural NetworkInformativenessLink PredictionNode ClassificationSimple yet Effective Graph Distillation via Clustering
Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to the tremendous computational overhead ne…
ClusteringGraph Representation LearningGraph SamplingNode ClassificationG1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
Although Large Language Models (LLMs) have demonstrated remarkable progress, their proficiency in graph-related tasks remains notably limited, hindering the development of truly general-purpose models. Previous attempts,…
Link PredictionNode Classificationreinforcement-learningReinforcement Learning+2How Particle System Theory Enhances Hypergraph Message Passing
Hypergraphs effectively model higher-order relationships in natural phenomena, capturing complex interactions beyond pairwise connections. We introduce a novel hypergraph message passing framework inspired by interacting…
Node ClassificationMessage-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modu…
Computational EfficiencyGraph LearningGraph Property PredictionNode Classification+2Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding
Graph Neural Networks (GNNs) excel at learning from pairwise interactions but often overlook multi-way and hierarchical relationships. Topological Deep Learning (TDL) addresses this limitation by leveraging combinatorial…
Node ClassificationRepresentation LearningScalable Graph Generative Modeling via Substructure Sequences
Graph neural networks (GNNs) has been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fu…
Graph ClassificationGraph LearningNode ClassificationTransfer LearningEC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are…
Graph LearningInference AttackLink PredictionNode ClassificationBeyond Node Attention: Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
Conventional Graph Neural Networks (GNNs) aggregate neighbor embeddings as holistic vectors, lacking the ability to identify fine-grained, direction-specific feature relevance. We propose MSH-GNN (Multi-Scale Harmonic Gr…
Graph Neural NetworkNode Classification