mlsanityData to 14 Oct 2026

Research on the application of graph data structure and graph neural network in node classification/clustering tasks

arXiv 2507.19527 ↗First posted 20 Jul 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine learning methods. This study investigates graph data structures, classical graph algorithms, and Graph Neural Networks (GNNs), providing comprehensive theoretical analysis and comparative evaluation. Through comparative experiments, we quantitatively assess performance differences between traditional algorithms and GNNs in node classification and clustering tasks. Results show GNNs achieve substantial accuracy improvements of 43% to 70% over traditional methods. We further explore integration strategies between classical algorithms and GNN architectures, providing theoretical guidance for advancing graph representation learning research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 20 Jul 2025.

Builds on

22 of 28 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  8. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  9. 2019Simplifying Graph Convolutional Networkscited by 1,477
  10. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  11. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  12. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
10 more
  1. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  2. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  3. 2020Simple and Deep Graph Convolutional Networkscited by 860
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  6. 2019Graph Neural Networks for Social Recommendationcited by 714
  7. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  8. 2019Neural Graph Collaborative Filteringcited by 661
  9. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  10. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311

Built on by

Not cited yet.