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Graph Neural Networks in Complex Data Pattern Recognition

Author(s):Rajat K. Desai1, Nitin P. Bansal2, Arvind L. Tiwari3

Affiliation: 1,2,3Department of Computer Science, Himachal Institute of Technology, Solan, Himachal Pradesh, India

Page No: 23-26

Volume issue & Publishing Year: Volume 2 Issue 2,Feb-2025

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI: https://doi.org/10.5281/zenodo.17330863

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Abstract:
Graph Neural Networks (GNNs) have emerged as a transformative approach in machine learning, capable of analyzing and recognizing complex data patterns where traditional models fall short. This study explores the application of GNNs in pattern recognition across diverse domains such as social networks, biomedical research, and traffic flow prediction. The paper discusses the architecture of GNNs, their ability to handle non-Euclidean data structures, and compares their efficiency with conventional deep learning models. Findings indicate that GNNs offer superior accuracy in detecting intricate relationships within highly connected data.

Keywords: Graph Neural Networks, Pattern Recognition, Machine Learning, Complex Data, Deep Learning

Reference:

  • 1. Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
  • 2. Veli?kovi?, P., Cucurull, G., Casanova, A., Romero, A., Li�, P., & Bengio, Y. (2018). Graph Attention Networks. International Conference on Learning Representations (ICLR).
  • 3. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2020). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4�24.
  • 4. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How Powerful Are Graph Neural Networks? International Conference on Learning Representations (ICLR).
  • 5. Hamilton, W. L., Ying, Z., & Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. Advances in Neural Information Processing Systems (NeurIPS), 30.
  • 6. Bronstein, M. M., Bruna, J., Cohen, T., & Velickovic, P. (2021). Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. arXiv preprint arXiv:2104.13478.
  • 7. Zhang, Z., Cui, P., & Zhu, W. (2020). Deep Learning on Graphs: A Survey. IEEE Transactions on Knowledge and Data Engineering, 34(1), 249�270.
  • 8. Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., & Leskovec, J. (2020). Open Graph Benchmark: Datasets for Machine Learning on Graphs. NeurIPS.
  • 9. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph Neural Networks: A Review of Methods and Applications. AI Open, 1, 57�81.
  • 10. Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., & Bresson, X. (2020). Benchmarking Graph Neural Networks. arXiv preprint arXiv:2003.00982.
  • 11. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018). Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD.
  • 12. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., & Weinberger, K. Q. (2019). Simplifying Graph Convolutional Networks. International Conference on Machine Learning (ICML).
  • 13. Rong, Y., Huang, W., Xu, T., & Huang, J. (2020). DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. ICLR.
  • 14. Li, Y., Han, J., Wu, X., & Liao, Y. (2021). Graph Representation Learning: Progress, Challenges, and Opportunities. Data Mining and Knowledge Discovery, 35(5), 1259�1290.