Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
We formulate an XAI-based model improvement approach for Graph Neural Networks (GNNs) for node classification, called Explanation Enhanced Graph Learning (EEGL). The goal is to improve predictive performance of GNN using…
Graph LearningGraph Neural NetworkNode ClassificationA Differential Geometric View and Explainability of GNN on Evolving Graphs
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a t…
Graph ClassificationLink PredictionNode ClassificationSiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal …
Graph Neural NetworkGraph Representation LearningNode ClassificationRepresentation LearningL^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explici…
Node ClassificationTask-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
A Knowledge Graph (KG) is a heterogeneous graph encompassing a diverse range of node and edge types. Heterogeneous Graph Neural Networks (HGNNs) are popular for training machine learning tasks like node classification an…
Knowledge GraphsLink PredictionNode ClassificationSpectral Invariant Learning for Dynamic Graphs under Distribution Shifts
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution settings only focuses on the time domain, …
Link PredictionNode ClassificationEntropy Aware Message Passing in Graph Neural Networks
Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an e…
Node ClassificationBetter Schedules for Low Precision Training of Deep Neural Networks
Low precision training can significantly reduce the computational overhead of training deep neural networks (DNNs). Though many such techniques exist, cyclic precision training (CPT), which dynamically adjusts precision …
Node ClassificationQuantizationSchedulingPolynormer: Polynomial-Expressive Graph Transformer in Linear Time
Graph transformers (GTs) have emerged as a promising architecture that is theoretically more expressive than message-passing graph neural networks (GNNs). However, typical GT models have at least quadratic complexity and…
Node ClassificationOpenGraph: Towards Open Graph Foundation Models
Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information …
Data AugmentationGraph LearningLanguage ModelingLanguage Modelling+4Pairwise Alignment Improves Graph Domain Adaptation
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the…
Domain AdaptationGRAPH DOMAIN ADAPTATIONNode ClassificationDecoupled Subgraph Federated Learning
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between di…
Federated LearningNode ClassificationROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open…
DenoisingGraph LearningNode ClassificationOpen Set LearningHyperdimensional Representation Learning for Node Classification and Link Prediction
We introduce Hyperdimensional Graph Learner (HDGL), a novel method for node classification and link prediction in graphs. HDGL maps node features into a very high-dimensional space (\textit{hyperdimensional} or HD space …
Classificationclass-incremental learningClass Incremental LearningGraph Learning+8GNNDLD: Graph Neural Network with Directional Label Distribution
By leveraging graph structure, Graph Neural Networks (GNN) have emerged as a useful model for graph-based datasets. While it is widely assumed that GNNs outperform basic neural networks, recent research shows that for …
Graph Neural NetworkNode ClassificationUnderstanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Diverging from extant approaches grounded in random walk analysis or particle systems, we approach this proble…
Node ClassificationLink Prediction with Physics-Inspired Graph Neural Networks
The message-passing mechanism underlying Graph Neural Networks (GNNs) is not naturally suited for heterophilic datasets, where adjacent nodes often have different labels. Most solutions to this problem remain confined to…
Graph Neural NetworkLink PredictionNode ClassificationPrediction+1Graph Parsing Networks
Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memor…
Graph ClassificationGraph ReconstructionNode ClassificationNode ClusteringA Simple and Yet Fairly Effective Defense for Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs to small adversarial perturbations. Exis…
Adversarial DefenseNode ClassificationBuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this challenge, existing studies generally generat…
ClassificationNode Classification