Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Unsupervised Episode Generation for Graph Meta-learning
We propose Unsupervised Episode Generation method called Neighbors as Queries (NaQ) to solve the Few-Shot Node-Classification (FSNC) task by unsupervised Graph Meta-learning. Doing so enables full utilization of the info…
Contrastive LearningMeta-LearningNode ClassificationSelf-Supervised LearningContrastive Meta-Learning for Few-shot Node Classification
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we ref…
ClassificationGraph MiningMeta-LearningNode ClassificationGPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neur…
Node ClassificationPolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs …
graph partitioningNode ClassificationReinforcement Learning (RL)Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Predicting node labels on a given graph is a widely studied problem with many applications, including community detection and molecular graph prediction. This paper considers predicting multiple node labeling functions o…
Community DetectionNode ClassificationTorsion Graph Neural Networks
Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the …
Graph Neural NetworkLink PredictionNode ClassificationGeometric Pooling: maintaining more useful information
Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical char…
Node ClassificationStructure-Aware DropEdge Towards Deep Graph Convolutional Networks
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, w…
Node ClassificationContrastive Disentangled Learning on Graph for Node Classification
Contrastive learning methods have attracted considerable attention due to their remarkable success in analyzing graph-structured data. Inspired by the success of contrastive learning, we propose a novel framework for con…
ClassificationContrastive LearningDisentanglementNode Classification+1CAT-Walk: Inductive Hypergraph Learning via Set Walks
Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-order interactions in complex systems. Representation learning for hypergraphs is essential for extracting patterns of the higher-order…
Hyperedge PredictionNode ClassificationRepresentation LearningMixed-Curvature Transformers for Graph Representation Learning papersreview
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage non-Euclidean spaces to embed such structures …
Graph ReconstructionGraph Representation LearningNode ClassificationRepresentation LearningFrom Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network arch…
Bilevel OptimizationGraph Neural NetworkNode ClassificationGraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs)…
BlockingClassificationNode ClassificationDual Node and Edge Fairness-Aware Graph Partition
Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typically consider node balance, a notion pur…
FairnessLink PredictionNode ClassificationThe Split Matters: Flat Minima Methods for Improving the Performance of GNNs
When training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value, a flat minimum in the loss landscape is …
Node ClassificationAccelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice. Existing …
Graph ReconstructionGraph Representation LearningLink PredictionNetwork Embedding+2A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can fool the GNN model to make desired predicti…
Node ClassificationPrivacy PreservingNodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range depend…
Graph structure learningimage-classificationImage ClassificationNode ClassificationLearning on Graphs under Label Noise
Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this tas…
Anomaly DetectionContrastive LearningGraph LearningGraph Neural Network+1A Simple and Scalable Graph Neural Network for Large Directed Graphs
Node classification is one of the hottest tasks in graph analysis. Though existing studies have explored various node representations in directed and undirected graphs, they have overlooked the distinctions of their capa…
ClassificationGraph Neural NetworkNode Classification