Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
TiGer: Self-Supervised Purification for Time-evolving Graphs
Time-evolving graphs, such as social and citation networks, often contain noise that distorts structural and temporal patterns, adversely affecting downstream tasks, such as node classification. Existing purification met…
Node ClassificationUsing Subgraph GNNs for Node Classification:an Overlooked Potential Approach
Previous studies have demonstrated the strong performance of Graph Neural Networks (GNNs) in node classification. However, most existing GNNs adopt a node-centric perspective and rely on global message passing, leading t…
ClassificationNode ClassificationNodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency
Aggregating information from neighboring nodes benefits graph neural networks (GNNs) in semi-supervised node classification tasks. Nevertheless, this mechanism also renders nodes susceptible to the influence of their nei…
Node ClassificationUnsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacen…
Link PredictionNetwork EmbeddingNode ClassificationPosition+1How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings
Low-dimensional embeddings are essential for machine learning tasks involving graphs, such as node classification, link prediction, community detection, network visualization, and network compression. Although recent stu…
Community DetectionLink PredictionNode ClassificationStatistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model
Graph neural networks (GNNs) are designed to process data associated with graphs. They are finding an increasing range of applications; however, as with other modern machine learning techniques, their theoretical underst…
Node ClassificationStochastic Block ModelAdversarial network embedding with bootstrapped representations for sparse networks
The inherent sparsity of real-world networks presents challenges in learning-rich embeddings and accurately reconstructing networks. To address these challenges, a novel method termed Adversarial Network Embedding with B…
Data VisualizationLink PredictionNetwork EmbeddingNode ClassificationHierarchical graph sampling based minibatch learning with chain preservation and variance reduction
Graph sampling based Graph Convolutional Networks (GCNs) decouple the sampling from the forward and backward propagation during minibatch training, which exhibit good scalability in terms of layer depth and graph size. W…
Graph SamplingNode ClassificationExploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Graph-structured data has become increasingly prevalent across various domains, raising the demand for effective models to handle graph tasks like node classification and link prediction. Traditional graph learning model…
BenchmarkingGraph LearningLink PredictionNode ClassificationGraph Augmentation for Cross Graph Domain Generalization
Cross-graph node classification, utilizing the abundant labeled nodes from one graph to help classify unlabeled nodes in another graph, can be viewed as a domain generalization problem of graph neural networks (GNNs) due…
Data AugmentationDomain GeneralizationNode ClassificationVGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning
Graph Neural Networks (GNNs) have gained attention for their ability to learn representations from graph data. Due to privacy concerns and conflicts of interest that prevent clients from directly sharing graph data with …
Adversarial AttackContrastive LearningFederated LearningNode ClassificationGraph Masked Language Models
Language Models (LMs) and Graph Neural Networks (GNNs) have shown great promise in their respective areas, yet integrating structured graph data with rich textual information remains challenging. In this work, we propose…
Graph LearningKnowledge GraphsNode ClassificationFHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths
Graph neural networks (GNNs) have emerged as the state of the art for a variety of graph-related tasks and have been widely used in Heterogeneous Graphs (HetGs), where meta-paths help encode specific semantics between va…
Graph EmbeddingLink PredictionNode ClassificationHetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks
Numerous real-world information networks form Heterogeneous Information Networks (HINs) with diverse objects and relations represented as nodes and edges in heterogeneous graphs. Similarity between nodes quantifies how c…
Graph Neural NetworkLink PredictionNode ClassificationHyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations (e.g., node dropping, edge perturbation,…
AttributeContrastive LearningNode ClassificationTowards Mechanistic Interpretability of Graph Transformers via Attention Graphs
We introduce Attention Graphs, a new tool for mechanistic interpretability of Graph Neural Networks (GNNs) and Graph Transformers based on the mathematical equivalence between message passing in GNNs and the self-attenti…
Node ClassificationGraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs
The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in text-attributed graphs, where shallow emb…
Node ClassificationUnlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transfor…
Graph ClassificationGraph Property PredictionGraph RegressionNode ClassificationCLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs
Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph prompting typically use feature prompts to…
Graph ReconstructionNode ClassificationPrompt LearningLiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection
Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in modeling data with graph structures, yet recent research reveals their susceptibility to adversarial attacks. Traditional attack methodologies, whi…
Node Classification