Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Global-Local Graph Neural Networks for Node-Classification
The task of graph node classification is often approached by utilizing a local Graph Neural Network (GNN), that learns only local information from the node input features and their adjacency. In this paper, we propose to…
ClassificationGraph Neural NetworkNode ClassificationPOWN: Prototypical Open-World Node Classification
We consider the problem of \textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not present during training. Existing methods dete…
ClassificationData AugmentationNode ClassificationSelf-Supervised LearningRobustness Inspired Graph Backdoor Defense
Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significan…
backdoor defenseGraph ClassificationNode ClassificationMulti-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling
In this paper, we tackle a new problem of \textit{multi-source unsupervised domain adaptation (MSUDA) for graphs}, where models trained on annotated source domains need to be transferred to the unsupervised target graph …
Domain AdaptationMeta-LearningMulti-Source Unsupervised Domain AdaptationNode Classification+1Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs
Heterogeneous graphs have attracted a lot of research interests recently due to the success for representing complex real-world systems. However, existing methods have two pain points in embedding them into low-dimension…
Link PredictionNode ClassificationRepresentation LearningClassic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported o…
Node ClassificationNode Property PredictionA Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized ex…
Graph ClassificationGraph RegressionNode ClassificationHierarchical Compression of Text-Rich Graphs via Large Language Models
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machine learning models, such as Graph Neural…
Node ClassificationGraphFM: A Comprehensive Benchmark for Graph Foundation Model
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream tasks. Despite extensive research into …
GPUGraph Neural NetworkLink Predictionmodel+3Holistic Memory Diversification for Incremental Learning in Growing Graphs
This paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continually train a graph model to handle new tasks while retaining its inference ability on pr…
Incremental LearningNode ClassificationRethinking the impact of noisy labels in graph classification: A utility and privacy perspective
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training dat…
ClassificationContrastive LearningData AugmentationGraph Classification+2Generating Human Understandable Explanations for Node Embeddings
Node embedding algorithms produce low-dimensional latent representations of nodes in a graph. These embeddings are often used for downstream tasks, such as node classification and link prediction. In this paper, we inves…
Link PredictionNode ClassificationEfficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs follow the message-passing paradigm, whe…
AttributeData AugmentationNode ClassificationTransfer Entropy in Graph Convolutional Neural Networks
Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In contrast to Convolutional Neural Networks, GCN's are designed to perform inference on graphs, where the num…
Node ClassificationLinkGPT: Teaching Large Language Models To Predict Missing Links
Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, particularly on Text-Attributed Graphs (TAG…
Few-Shot LearningLink PredictionNode ClassificationRerankingGraph Mining under Data scarcity
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning for graphs has been introduced to overc…
ClassificationFew-Shot LearningGraph LearningGraph Mining+2Cooperative Meta-Learning with Gradient Augmentation
Model agnostic meta-learning (MAML) is one of the most widely used gradient-based meta-learning, consisting of two optimization loops: an inner loop and outer loop. MAML learns the new task from meta-initialization param…
Few-Shot Image Classificationimage-classificationImage ClassificationMeta-Learning+1GENIE: Watermarking Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have become invaluable intellectual property in graph-based machine learning. However, their vulnerability to model stealing attacks when deployed within Machine Learning as a Service (MLaaS)…
Backdoor AttackDrug DiscoveryGraph ClassificationLink Prediction+3PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-ra…
Graph ClassificationGraph Neural NetworkGraph RegressionNode ClassificationNoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node labels, which are challenging to obtain …
Node Classification