Node Classification
781 papers with code • 122 benchmarks • 68 datasets
Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.
Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.
( Image credit: Fast Graph Representation Learning With PyTorch Geometric )
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Latest papers with no code
You do not have to train Graph Neural Networks at all on text-attributed graphs
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities.
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer
Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level.
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis
Subsequently, PNCA is used to analyze the mainstream polynomial filters, and a novel simple basis that decouples the positive and negative activation and fully utilizes graph structure information is designed.
Hyperbolic Heterogeneous Graph Attention Networks
Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space.
Fair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation.
Graph Neural Networks for Binary Programming
This paper investigates a link between Graph Neural Networks (GNNs) and Binary Programming (BP) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging problems.
Generative-Contrastive Heterogeneous Graph Neural Network
In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and discriminators for downstream tasks.
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs.
Graph Neural Aggregation-diffusion with Metastability
Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied.
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.