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Node Classification

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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.

<span style="color:grey; opacity: 0.6">( Image credit: Fast Graph Representation Learning With PyTorch Geometric )</span>

논문

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Node Classification

논문

GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights

Demystifying Distributed Training of Graph Neural Networks for Link Prediction

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models

PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark

Graph Semi-Supervised Learning for Point Classification on Data Manifolds

Wasserstein Hypergraph Neural Network

Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs

Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning

Weak Supervision for Real World Graphs

HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport

DeGLIF for Label Noise Robust Node Classification using GNNs

Improving the Effective Receptive Field of Message-Passing Neural Networks

Graph Positional Autoencoders as Self-supervised Learners

Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs

Directed Homophily-Aware Graph Neural Network

Simple yet Effective Graph Distillation via Clustering

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning

How Particle System Theory Enhances Hypergraph Message Passing

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding

Scalable Graph Generative Modeling via Substructure Sequences

Beyond Node Attention: Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression

Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening

Unsupervised Graph Clustering with Deep Structural Entropy

MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow

Unlearning Algorithmic Biases over Graphs

Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics

GraphFLEx: Structure Learning Framework for Large Expanding Graphs

It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph

Finding Counterfactual Evidences for Node Classification

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning

Efficient Mixed Precision Quantization in Graph Neural Networks