Abstract
Network structure is formed by intricate connections between nodes, exploring and learning the network topological structural features has a profound impact in the field of network representation learning. Role refers to a collection of nodes with similar structural features in the network. So network representation learning that preserves node structure in a low-dimensional vector representation space is also known as role discovery, which focuses on partitioning the network into different sets of roles based on structural features. Although existing methods for network structure embedding have made some progress in role discovery tasks, most of them focus on the local structural features to generate node representations, resulting in the inability to learn multiaspect structural features of roles. Therefore, we propose a network structure embedding model URold, which uses role domain feature to enhance node structure representation capabilities and learn the proximity between roles. We conduct role discovery experiments on six real-world networks, and compare with eight state-of-the-art network structure embedding algorithms. The results show that our method URold achieves the best performance and demonstrates excellent role discovery ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, N.K., et al.: Role-based graph embeddings. IEEE Trans. Knowl. Data Eng. 34(5), 2401–2415 (2022)
Cai, J., Wang, S., Xu, C., Guo, W.: Unsupervised deep clustering via contractive feature representation and focal loss. Pattern Recogn. 123, 108386 (2022)
Cao, S., Lu, W., Xu, Q.: Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, vol. 19–23-, pp. 891–900. ACM (2015)
Donnat, C., Zitnik, M., Hallac, D., Leskovec, J.: Learning structural node embeddings via diffusion wavelets. In: SIGKDD, pp. 1320–1329. ACM (2018)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13–17-, pp. 855–864. ACM (2016)
Guo, X., Zhang, W., Wang, W., Yu, Y., Wang, Y., Jiao, P.: Role-oriented graph auto-encoder guided by structural information. In: DASFAA, vol. 12113, pp. 466–481 (2020)
Guo, Z., Wang, F., Yao, K., Liang, J., Wang, Z.: Multi-scale variational graph autoencoder for link prediction. In: WSDM, pp. 334–342 (2022)
Gupte, P.V., Ravindran, B., Parthasarathy, S.: Role discovery in graphs using global features: algorithms, applications and a novel evaluation strategy. In: ICDE, pp. 771–782. IEEE (2017)
Heimann, M., Shen, H., Safavi, T., Koutra, D.: Regal: representation learning-based graph alignment. In: CIKM, pp. 117–126. ACM (2018)
Henderson, K., et al.: Rolx: structural role extraction & mining in large graphs. In: SIGKDD, pp. 1231–1239. ACM (2012)
Henderson, K., et al.: It’s who you know: graph mining using recursive structural features. In: SIGKDD, pp. 663–671. ACM (2011)
Jiao, P., Tian, Q., Zhang, W., Guo, X., Jin, D., Wu, H.: Role discovery-guided network embedding based on autoencoder and attention mechanism. IEEE Trans. Cybern. 53(1), 1–14 (2023)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web. WWW ’10, pp. 641–650. Association for Computing Machinery, New York (2010). https://doi.org/10.1145/1772690.1772756
Luo, J., Xiao, S., Jiang, S., Gao, H., Xiao, Y.: ripple2vec: Node embedding with ripple distance of structures. Data Sci. Eng. 7(2), 156–174 (2022)
Ma, X., Qin, G., Qiu, Z., Zheng, M., Wang, Z.: Riwalk: fast structural node embedding via role identification. In: ICDM, vol. 2019-, pp. 478–487 (2019)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104–036104 (2006)
Nikolentzos, G., Vazirgiannis, M.: Learning structural node representations using graph kernels. IEEE Trans. Knowl. Data Eng. 33(5), 2045–2056 (2021)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Ribeiro, L., Saverese, P., Figueiredo, D.: struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 129685, pp. 385–394. ACM (2017)
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the National Conference on Artificial Intelligence, vol. 6, pp. 4292–4293 (2015)
Tian, Q., Zhang, W., Jiao, P., Zhong, K., Wu, N., Pan, L.: Integrating higher-order features for structural role discovery. In: LNICST, vol. 451, pp. 244–258 (2022)
Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: Verse: versatile graph embeddings from similarity measures. In: WWW, pp. 539–548 (2018)
Tu, K., Cui, P., Wang, X., Yu, P., Zhu, W.: Deep recursive network embedding with regular equivalence. In: KDD, pp. 2357–2366. ACM (2018)
Van Der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2625 (2008)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13–17-, pp. 1225–1234. ACM (2016)
Xia, Y., Xia, C.Q., Pan, X., Shen, H.B.: Graphbind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues. Nucleic Acids Res. 49(9), e51–e51 (2021)
Zhang, W., Guo, X., Wang, W., Tian, Q., Pan, L., Jiao, P.: Role-based network embedding via structural features reconstruction with degree-regularized constraint. Knowl.-Based Syst. 218, 106872 (2021)
Zhang, X., Xie, K., Wang, S., Huang, Z.: Learning based proximity matrix factorization for node embedding. In: SIGKDD, pp. 2243–2253 (2021)
Zhang, Z., Cui, P., Wang, X., Pei, J., Yao, X., Zhu, W.: Arbitrary-order proximity preserved network embedding. In: SIGKDD, pp. 2778–2786. ACM (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ge, L., Li, H., Lin, Y., Xie, J. (2024). Network Structure Embedding Method Based on Role Domain Feature. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-7019-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7018-6
Online ISBN: 978-981-99-7019-3
eBook Packages: Computer ScienceComputer Science (R0)