Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification
The varying degrees of homophily and heterophily in real-world graphs persistently constrain the universality of graph neural networks (GNNs) for node classification. Adopting a data-centric perspective, this work reveal…
Mixture-of-ExpertsNode ClassificationRepresentation LearningRethinking Tokenized Graph Transformers for Node Classification
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which transforms the input graph into token s…
ClassificationNode ClassificationRepresentation LearningUnified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. Howev…
Community DetectionGraph LearningGraph-To-Graph TranslationKnowledge Graph Completion+2Deep Semantic Graph Learning via LLM based Node Enhancement
Graph learning has attracted significant attention due to its widespread real-world applications. Current mainstream approaches rely on text node features and obtain initial node embeddings through shallow embedding lear…
Graph LearningNode ClassificationGraph Neural Networks at a Fraction
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. Wit…
Graph ClassificationLink PredictionNode ClassificationGNNs Getting ComFy: Community and Feature Similarity Guided Rewiring
Maximizing the spectral gap through graph rewiring has been proposed to enhance the performance of message-passing graph neural networks (GNNs) by addressing over-squashing. However, as we show, minimizing the spectral g…
Node ClassificationLeveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning
Graph representation learning has emerged as a cornerstone for tasks like node classification and link prediction, yet prevailing self-supervised learning (SSL) methods face challenges such as computational inefficiency,…
Bayesian InferenceGraph Representation LearningLink PredictionNode Classification+3When Do LLMs Help With Node Classification? A Comprehensive Analysis
Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-based approaches for this task. Although m…
Node ClassificationSpectro-Riemannian Graph Neural Networks
Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic (negative curvature) and spherical (pos…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningNode Classification and Search on the Rubik's Cube Graph with GNNs
This study focuses on the application of deep geometric models to solve the 3x3x3 Rubik's Cube. We begin by discussing the cube's graph representation and defining distance as the model's optimization objective. The dist…
Node ClassificationRubik's CubeContrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to Global
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in various graph representation learning tasks. However, most existing GNNs focus primarily on capturing local information through explicit graph co…
Contrastive LearningGraph Representation LearningNode ClassificationPseudo Label+1Beyond Message Passing: Neural Graph Pattern Machine
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance. However, most existin…
Graph ClassificationGraph LearningGraph RegressionLink Prediction+2Random Walk Guided Hyperbolic Graph Distillation
Graph distillation (GD) is an effective approach to extract useful information from large-scale network structures. However, existing methods, which operate in Euclidean space to generate condensed graphs, struggle to ca…
Continual LearningGraph LearningLink PredictionNode ClassificationMamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to conv…
Graph Neural NetworkGraph Representation LearningMambaNode Classification+1GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. However, unlike graph data, natural language…
Graph Question AnsweringGraph SamplingNode ClassificationQuestion Answering+1Personalized Layer Selection for Graph Neural Networks
Graph Neural Networks (GNNs) combine node attributes over a fixed granularity of the local graph structure around a node to predict its label. However, different nodes may relate to a node-level property with a different…
Metric LearningNode ClassificationComprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs
The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medic…
DiagnosticFew-Shot LearningHallucinationNode Classification+2HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks
Representation learning on heterogeneous text-rich networks (HTRNs), which consist of multiple types of nodes and edges with each node associated with textual information, is essential for various real-world applications…
Link PredictionNode ClassificationPrompt LearningRepresentation LearningOptimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks
Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports gov…
Fraud DetectionGraph Representation LearningIncremental LearningNode Classification+1Each Graph is a New Language: Graph Learning with LLMs
Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs to understand or aggregate LLM-generate…
AttributeGraph LearningNode Classification