Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Graph Neural Networks for Binary Programming
This paper investigates a link between Graph Neural Networks (GNNs) and Binary Programming (BP) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging problems. By analyzi…
Graph Representation LearningNode ClassificationRepresentation LearningSpectral Graph Pruning Against Over-Squashing and Over-Smoothing
Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former results from topological bottlenecks that…
Node ClassificationTheoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., privileging celebrities and othe…
Node ClassificationGenerative-Contrastive Heterogeneous Graph Neural Network
Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL), contrastive learning (CL)-based Hetero…
Contrastive LearningData AugmentationGraph Neural NetworkLink Prediction+2GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting the right GL model becomes increasingly…
Graph LearningLink PredictionmodelModel Selection+1HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings in such graphs is a critical problem in …
Graph EmbeddingGraph Representation LearningLink PredictionNode Classification+1HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances…
Hypergraph representationsNode ClassificationRepresentation LearningSelf-Supervised LearningGraph Neural Aggregation-diffusion with Metastability
Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models hav…
Node ClassificationBeyond the Known: Novel Class Discovery for Open-world Graph Learning
Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emerge on unlabeled testing nodes. However, …
Graph LearningGraph Neural NetworkNode ClassificationNovel Class DiscoveryVariational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph st…
Graph Representation LearningInductive LearningNode ClassificationRepresentation LearningLearn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. Current studies on graph heterophi…
Graph LearningGraph Neural NetworkNode ClassificationHealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks
While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly lim…
Graph AttentionNode ClassificationChebMixer: Efficient Graph Representation Learning with MLP Mixer
Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various graph mining tasks. However, graph Transfor…
Graph MiningGraph Representation LearningImage SegmentationMedical Image Segmentation+3A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. Howev…
Federated LearningGraph Neural NetworkInference AttackMembership Inference Attack+1Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks
Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervis…
Graph Neural NetworkNode ClassificationExploring the Potential of Large Language Models in Graph Generation
Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph g…
Drug DiscoveryGraph GenerationNode ClassificationOpen-World Semi-Supervised Learning for Node Classification
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.…
ClassificationContrastive LearningNode ClassificationOpen-World Semi-Supervised LearningGeneration is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribution differences between different class…
Anomaly DetectionGraph Anomaly DetectionGraph ClassificationGraph Neural Network+1Robust Subgraph Learning by Monitoring Early Training Representations
Graph neural networks (GNNs) have attracted significant attention for their outstanding performance in graph learning and node classification tasks. However, their vulnerability to adversarial attacks, particularly throu…
Adversarial RobustnessDecision MakingGraph LearningNode ClassificationReduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models
The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research. Despite widespread techniques used to diminish the over-fitting issue such as data augmentat…
ClassificationData AugmentationEnsemble LearningGraph Learning+3