Node Classification
795 papers with code • 121 benchmarks • 69 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
Pairwise Alignment Improves Graph Domain Adaptation
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing.
Graph Parsing Networks
GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact.
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results.
Can GNN be Good Adapter for LLMs?
In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs.
Endowing Pre-trained Graph Models with Provable Fairness
Furthermore, with GraphPAR, we quantify whether the fairness of each node is provable, i. e., predictions are always fair within a certain range of sensitive attribute semantics.
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally.
GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly
Additionally, our experiments with synthetic metagenomic datasets reveal that incorporating the graph structure and the GNN enhances our detection performance.
Disambiguated Node Classification with Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains.
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks.