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Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning

Published 30 Jul 2024 in cs.LG and cs.AI | (2407.20648v2)

Abstract: Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing methods often rely on domain-specific predefined meta-paths, which are coarse-grained and focus solely on aspects like node type, limiting their ability to capture complex interactions. We introduce MF2Vec, a model that uses multi-faceted (fine-grained) paths instead of predefined meta-paths. MF2Vec extracts paths via random walks and generates multi-faceted vectors, ignoring predefined schemas. This method learns diverse aspects of nodes and their relationships, constructs a homogeneous network, and creates node embeddings for classification, link prediction, and clustering. Extensive experiments show that MF2Vec outperforms existing methods, offering a more flexible and comprehensive framework for analyzing complex networks. The code is available at https://anonymous.4open.science/r/MF2Vec-6ABC.

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References (28)
  1. Relational Graph Attention Networks. arXiv:1904.05811.
  2. PME: projected metric embedding on heterogeneous networks for link prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 1177–1186.
  3. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29.
  4. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 135–144.
  5. Is a single embedding enough? learning node representations that capture multiple social contexts. In The world wide web conference, 394–404.
  6. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1797–1806.
  7. MECCH: metapath context convolution-based heterogeneous graph neural networks. Neural Networks, 170: 266–275.
  8. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The Web Conference 2020, 2331–2341.
  9. HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning. Knowledge-Based Systems, 279: 110930.
  10. Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
  11. An attention-based graph neural network for heterogeneous structural learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, 4132–4139.
  12. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144.
  13. GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. ACM Transactions on Information Systems (TOIS), 40(3): 1–35.
  14. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
  15. László, B. A. 2014. Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Basic Books.
  16. Is a single vector enough? exploring node polysemy for network embedding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 932–940.
  17. Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 1150–1160.
  18. The structure and dynamics of networks. Princeton university press.
  19. Unsupervised differentiable multi-aspect network embedding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1435–1445.
  20. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, 593–607. Springer.
  21. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2): 357–370.
  22. MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning. arXiv:2106.09289.
  23. Visualizing data using t-SNE. Journal of machine learning research, 9(11).
  24. Graph attention networks. stat, 1050(20): 10–48550.
  25. Heterogeneous graph attention network. In The world wide web conference, 2022–2032.
  26. Self-supervised heterogeneous graph neural network with co-contrastive learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 1726–1736.
  27. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 793–803.
  28. Shne: Representation learning for semantic-associated heterogeneous networks. In Proceedings of the twelfth ACM international conference on web search and data mining, 690–698.

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