Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to …
Node ClassificationOut of Distribution (OOD) DetectionUncertainty QuantificationMixture of Weak & Strong Experts on Graphs
Realistic graphs contain both (1) rich self-features of nodes and (2) informative structures of neighborhoods, jointly handled by a Graph Neural Network (GNN) in the typical setup. We propose to decouple the two modaliti…
Graph Neural NetworkNode ClassificationDirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, …
Node ClassificationPredicting Properties of Nodes via Community-Aware Features
This paper shows how information about the network's community structure can be used to define node features with high predictive power for classification tasks. To do so, we define a family of community-aware node featu…
ClassificationNode ClassificationEdge2Node: Reducing Edge Prediction to Node Classification
Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential links between nodes in a graph) remains a challenging problem for these models. A co…
Graph Neural NetworkLink PredictionLink Property PredictionNode Classification+1Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
With the prosperity of contrastive learning for visual representation learning (VCL), it is also adapted to the graph domain and yields promising performance. However, through a systematic study of various graph contrast…
Contrastive LearningGraph ClassificationGraph LearningInductive Bias+2Cooperative Network Learning for Large-Scale and Decentralized Graphs
Graph research, the systematic study of interconnected data points represented as graphs, plays a vital role in capturing intricate relationships within networked systems. However, in the real world, as graphs scale up, …
Graph LearningLink PredictionNode ClassificationPrivacy PreservingCalibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
As a powerful framework for graph representation learning, Graph Neural Networks (GNNs) have garnered significant attention in recent years. However, to the best of our knowledge, there has been no formal analysis of the…
Graph Representation LearningNode ClassificationRepresentation LearningVIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
Class imbalance in graph data presents significant challenges for node classification. While existing methods, such as SMOTE-based approaches, partially mitigate this issue, they still exhibit limitations in constructing…
Contrastive LearningNode ClassificationSelf-Supervised LearningVariational InferenceHyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datase…
Inductive BiasLink PredictionMeta-LearningNode ClassificationRethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance…
Node ClassificationBLIS-Net: Classifying and Analyzing Signals on Graphs
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many…
ClassificationGraph ClassificationNode ClassificationPSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The …
Contrastive LearningGraph ClassificationNode ClassificationResurrecting Label Propagation for Graphs with Heterophily and Label Noise
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph mode…
DenoisingNode ClassificationGraph Agent: Explicit Reasoning Agent for Graphs
Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpreta…
Graph EmbeddingKnowledge GraphsLink PredictionNode ClassificationGraph Neural Networks with a Distribution of Parametrized Graphs
Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, suc…
Graph RegressionNode ClassificationGenerative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity betwee…
Contrastive LearningGraph ClassificationLink PredictionNode Classification+2Deceptive Fairness Attacks on Graphs via Meta Learning
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? We answer this question via a bi-level op…
Adversarial RobustnessFairnessGraph LearningMeta-Learning+1Hierarchical Randomized Smoothing
Real-world data is complex and often consists of objects that can be decomposed into multiple entities (e.g. images into pixels, graphs into interconnected nodes). Randomized smoothing is a powerful framework for making …
Node ClassificationA Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thi…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+3