Node Classification
769 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 )
Libraries
Use these libraries to find Node Classification models and implementationsSubtasks
Latest papers with no code
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference.
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance.
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments.
Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks
Graph data, also known as complex network data, is omnipresent across various domains and applications.
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
Robust Subgraph Learning by Monitoring Early Training Representations
However, their vulnerability to adversarial attacks, particularly through susceptible nodes, poses a challenge in decision-making.
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models
The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research.
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
EEGL is an iterative self-improving algorithm, which starts with a learned "vanilla" GNN, and repeatedly uses frequent subgraph mining to find relevant patterns in explanation subgraphs.