Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
Graphs with heterophily, where adjacent nodes carry different labels, are prevalent in real-world applications, from social networks to molecular interactions. However, existing spectral Graph Neural Network (GNN) approa…
Graph ClassificationGraph Neural NetworkNode ClassificationGraphPI: Efficient Protein Inference with Graph Neural Networks
The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the sc…
Computational EfficiencyGraph Neural NetworkNode ClassificationA Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
Knowledge graphs automatically constructed from text are increasingly used in real-world applications. However, their inherent noise, fragmentation, and semantic inconsistencies significantly affect the performance of Gr…
Node ClassificationKnowledge GraphsGRAFT: Auditing Graph Neural Networks via Global Feature Attribution
Graph Neural Networks (GNNs) achieve strong performance on node classification tasks but remain difficult to interpret, particularly with respect to which input features drive their predictions. Existing global GNN expla…
Node ClassificationFeature ImportanceTransfer LearningRethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interactions and inter-label dependencies.However, it remains unclear whethe…
Node ClassificationGraph LearningBatch Normalization for Neural Networks on Complex Domains
Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of principled Riemannian analogs of fundamental building blocks in deep ne…
Node ClassificationAction RecognitionAitchison Embeddings for Learning Compositional Graph Representations
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learne…
Representation LearningNode ClassificationLink PredictionGraph EmbeddingA Unified Framework of Hyperbolic Graph Representation Learning Methods
Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embe…
Graph Representation LearningNode ClassificationLink PredictionMini-Batch Class Composition Bias in Link Prediction
Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect G…
Node ClassificationLink Prediction$\mathcal{S}^2$IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction
Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabilities of large language models (LLMs). However, while syntactic struc…
Node ClassificationMochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as…
Graph ClassificationNode ClassificationLink PredictionF\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by significant computational overhead and reliance on strong homophily …
Computational EfficiencyNode ClassificationSpectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
Graph Neural Networks (GNNs) excel on relational data, but standard benchmarks unrealistically assume the graph is centrally available. In practice, settings such as Federated Graph Learning, distributed systems, and pri…
Node ClassificationLink PredictionGraph LearningLearning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each ne…
Node ClassificationGraph GenerationGraph LearningFOCAL-Attention for Heterogeneous Multi-Label Prediction
Heterogeneous graphs have attracted increasing attention for modeling multi-typed entities and relations in complex real-world systems. Multi-label node classification on heterogeneous graphs is challenging due to struct…
Node ClassificationInductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt GNNs to heterophilic graphs through non-l…
Node ClassificationCausal InferenceGraph LearningHopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommendation systems. Current GNN-based approaches suffer from shallow text …
Recommendation SystemsNode ClassificationLink PredictionGraph self-supervised learning based on frequency corruption
Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web applications. However, existing methods often underuse high-frequency s…
Self-Supervised LearningNode ClassificationTransfer LearningLearning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static and rigid routing strategies that enforce a uniform expert budget or…
Node ClassificationNeighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer vision. However, their efficacy is severely compromised by the inherent ho…
Node ClassificationGraph Learning