Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference cha…
Computational EfficiencyGraph ClassificationGraph ClusteringGraph Learning+4Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learn…
Adversarial AttackNode ClassificationSparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
The growing computational demands posed by increasingly number of neural network's parameters necessitate low-memory-consumption training approaches. Previous memory reduction techniques, such as Low-Rank Adaptation (LoR…
Link PredictionMachine TranslationNode ClassificationText GenerationEncoder Embedding for General Graph and Node Classification
Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a genera…
ClassificationNode ClassificationAGS-GNN: Attribute-guided Sampling for Graph Neural Networks
We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and hete…
AttributeGraph SamplingNode ClassificationHC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph da…
DecoderGraph ClassificationGraph Representation LearningNode Classification+1Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification
Hypergraphs are widely employed to represent complex higher-order relations in real-world applications. Most hypergraph learning research focuses on node-level or edge-level tasks. A practically relevant but more challen…
Node ClassificationAutomated Loss function Search for Class-imbalanced Node Classification
Class-imbalanced node classification tasks are prevalent in real-world scenarios. Due to the uneven distribution of nodes across different classes, learning high-quality node representations remains a challenging endeavo…
Node ClassificationSimilarity-Navigated Conformal Prediction for Graph Neural Networks
Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarant…
Conformal PredictionNode ClassificationPredictionvalidAnalysis of Corrected Graph Convolutions
Machine learning for node classification on graphs is a prominent area driven by applications such as recommendation systems. State-of-the-art models often use multiple graph convolutions on the data, as empirical eviden…
ClassificationNode ClassificationRecommendation SystemsStochastic Block ModelLOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the superior performance of GNNs on different…
Graph Neural NetworkLanguage ModelingLanguage ModellingLarge Language Model+1Node-Time Conditional Prompt Learning In Dynamic Graphs
Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have eme…
Link PredictionNode ClassificationPrompt LearningUtilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks
Graph Neural Networks (GNNs) are effective for node classification in graph-structured data, but they lack explainability, especially at the global level. Current research mainly utilizes subgraphs of the input as local …
Node ClassificationConditional Shift-Robust Conformal Prediction for Graph Neural Network
Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their limited ability to provide robust uncertain…
Conformal PredictionGraph Neural NetworkNode ClassificationPrediction+1Leveraging Discourse Structure for Extractive Meeting Summarization
We introduce an extractive summarization system for meetings that leverages discourse structure to better identify salient information from complex multi-party discussions. Using discourse graphs to represent semantic re…
Extractive SummarizationMeeting SummarizationNode ClassificationPerception-Inspired Graph Convolution for Music Understanding Tasks
We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensio…
Graph ClassificationGraph LearningLink PredictionNode Classification+1Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbations in the input graph, which makes the…
Node ClassificationA Survey of Large Language Models for Graphs
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, suc…
Graph LearningLink PredictionNode ClassificationSurveyConditional Local Feature Encoding for Graph Neural Networks
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its l…
Graph ClassificationGraph RegressionLink PredictionNode ClassificationCoefficient Decomposition for Spectral Graph Convolution
Spectral graph convolutional network (SGCN) is a kind of graph neural networks (GNN) based on graph signal filters, and has shown compelling expressivity for modeling graph-structured data. Most SGCNs adopt polynomial fi…
Node Classification