Node Classification
789 papers with code • 122 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
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Latest papers
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.
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs
In this paper, we propose a new architecture, HyperBERT, a mixed text-hypergraph model which simultaneously models hypergraph relational structure while maintaining the high-quality text encoding capabilities of a pre-trained BERT.
Rethinking Node-wise Propagation for Large-scale Graph Learning
However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required.
Classifying Nodes in Graphs without GNNs
Recently, distillation methods succeeded in eliminating the use of GNNs at test time but they still require them during training.
Similarity-based Neighbor Selection for Graph LLMs
Our research further underscores the significance of graph structure integration in LLM applications and identifies key factors for their success in node classification.