Abstract
Network embedding is a representation learning paradigm that seeks to learn a compact low-dimensional distributed vector representation for each vertex in the network; this learned low-dimensional vector representation can thus be used for different machine learning tasks. Over the years, so many network embedding models have been worked upon based on several approaches. In this paper, we study vector embeddings of 10 different representation learning models, with the sole aim of carrying out two machine learning tasks on these learned representations – unsupervised community clustering and link prediction analysis. The goal is to compare the output of these tasks using the 10 models, and draw inference based on the obtained results. We analyze the results using 4 link prediction baseline heuristic measures for the link prediction analysis; and a combination of silhouette score analysis and dissimilarity metric index for the community analysis.
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References
Perozzi, B., Kulkarni, V., Chen, H., Skiena, S.: Don’t Walk, Skip!: online learning of multi-scale network embeddings. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 258–265 (2017)
Patel, A.S., Ramakrishna, R.M., Jai, M., Singh, K., Sivadasan, N., Balasubramanian, V.N.: HIVEC: a hierarchical approach for vector representation learning of graphs (2018)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD, pp. 855–864 (2016)
Jundong, L., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment, arXiv abs/1706.01860, pp. 1–10 (2017)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD, pp. 701–710 (2014)
Bandyopadhyay, S., Kara, H., Kannan, A., Murty, M.N.: FSCNMF: fusing structure and content via non-negative matrix factorization for embedding information networks, Conference Submission, arXiv:1804.05313 [cs.SI] (2018)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI) (2015)
Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pp. 891–900 (2015)
Donnat, C., Zitnik, M., Hallac, D., Leskovec, J.: Learning structural node embeddings via diffusion wavelets. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1320–1329 (2018)
Yang, H., Pan, S., Zhang, P., Chen, L., Lian, D., Zhang, C.: Binarized attributed network embedding. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM), pp. 393–401 (2019)
Punj, G., Stewart, D.W.: Cluster analysis in marketing research: review and suggestions for application. J. Mark. Res. 20(2), 134–148 (1983)
Setyaningsih, S.: Using cluster analysis study to examine the successful performance entrepreneur in Indonesia. Procedia Econ. Finance 4, 286–298 (2012)
Ullman, S., Poggio, T., Harari, D., Zysman, D., Seibert, D.: Unsupervised Learning Clustering, Centre for Brains, Minds, and Machines, pp. 1–54 (2014)
Rozemberczki, B., Davies, R., Sarkar, R., Sutton, C.: GEMSEC: graph embedding with self clustering. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 65–72 (2019)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD, pp. 243–252 (2010)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: WWW (2015)
Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 23(3), 447–478 (2011)
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Oluigbo, I., Haddad, M., Seba, H. (2020). Evaluating Network Embedding Models for Machine Learning Tasks. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_76
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DOI: https://doi.org/10.1007/978-3-030-36687-2_76
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