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A Network Embedding and Clustering Algorithm for Expert Recommendation Service

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Book cover Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

Abstract

Network embedding algorithm is dedicated to learning the low-dimensional representation of network nodes. The feature representations can be used as features of various tasks based on graphs, including classification, clustering, link prediction and visualization. Currently, network embedding algorithms have evolved from considering structures only to considering structures and contents both. However, how to effectively integrate the high-order proximity and node content of the network structure is still a problem to be solved. We propose a new network embedding and clustering algorithm in this paper. We obtain the high-order proximity representation of the information network structure, and the fusion node content completes the low-dimensional representation of the node features, so as to complete the network node clustering for the input of the spectral clustering. In order to further verify the value of the algorithm, we apply the clustering results to the field of expert recommendation, and make influence and activity assessments for domain experts to achieve more valuable expert recommendations. The experimental results show that the proposed algorithm will obtain higher clustering accuracy and excellent expert recommendation results.

This work was jointly supported by the Scientific and Technological Support Project of Jiangsu Province under Grant BE2016776, the “333” project of Jiangsu Province under Grant BRA2017228 and the Talent Project in Six Fields of Jiangsu Province under Grant 2015-JNHB-012.

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References

  1. 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)

    Google Scholar 

  2. Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 731–739. ACM (2017)

    Google Scholar 

  3. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)

    Google Scholar 

  4. 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, pp. 855–864. ACM (2016)

    Google Scholar 

  5. Yang, C., Sun, M., Liu, Z., Tu, C.: Fast network embedding enhancement via high order proximity approximation. In: Proceedings of the 2017 International Joint Conference on Artificial Intelligence, pp. 3894–3900. AAAI (2017)

    Google Scholar 

  6. Zhu, D., Cui, P., Zhang, Z., Pei, J., Zhu, W.: High-order proximity preserved embedding for dynamic networks. IEEE Trans. Knowl. Data Eng. 30(11), 2134–2144 (2018)

    Google Scholar 

  7. Bandyopadhyay, S., Kara, H., Kannan, A., Murty, M.N.: FSCNMF: fusing structure and content via non-negative matrix factorization for embedding information networks. arXiv preprint arXiv:1804.05313 (2018)

  8. Zhang, Z., Cui, P., Wang, X., Pei, J., Yao, X., Zhu, W.: Arbitrary-order proximity preserved network embedding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2778–2786. ACM (2018)

    Google Scholar 

  9. 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. ACM (2015)

    Google Scholar 

  10. Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2019)

    Article  Google Scholar 

  11. Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114. ACM (2016)

    Google Scholar 

  12. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the 31st Conference on Artificial Intelligence, pp. 203–209. AAAI (2017)

    Google Scholar 

  13. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)

    Article  Google Scholar 

  14. Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2018)

    Article  Google Scholar 

  15. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017)

  16. Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)

    Article  Google Scholar 

  17. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  18. 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. ACM (2017)

    Google Scholar 

  19. Abu-El-Haija, S., Perozzi, B., Al-Rfou, R., Alemi, A.: Watch your step: Learning graph embeddings through attention. arXiv preprint arXiv:1710.09599 (2017)

  20. Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, pp. 2111–2117. AAAI (2015)

    Google Scholar 

  21. Zhang, D., Yin, J., Zhu, X., Zhang, C.: Collective classification via discriminative matrix factorization on sparsely labeled networks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1563–1572. ACM (2016)

    Google Scholar 

  22. Saha, A., Sindhwani, V.: Learning evolving and emerging topics in social media: a dynamic NMF approach with temporal regularization. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining, pp. 693–702. ACM (2012)

    Google Scholar 

  23. Zhang, D., Yin, J., Zhu, X., Zhang, C.: User profile preserving social network embedding. In: Proceedings of the 2017 International Joint Conference on Artificial Intelligence, pp. 3378–3384 (2017)

    Google Scholar 

  24. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  25. Tantipathananandh, C., Berger-Wolf, T.Y.: Finding communities in dynamic social networks. In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining, pp. 1236–1241. IEEE (2011)

    Google Scholar 

  26. Cheng, J., et al.: Voting simulation based agglomerative hierarchical method for network community detection. Sci. Rep. 8(1), 8064 (2018)

    Article  Google Scholar 

  27. de Guzzi Bagnato, G., Ronqui, J.R.F., Travieso, G.: Community detection in networks using self-avoiding random walks. Physica A 505, 1046–1055 (2018)

    Google Scholar 

  28. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Proceedings of the International Conference on Neural Information Processing Systems, pp. 849–856. MIT Press (2001)

    Google Scholar 

  29. Huang, C., Yao, L., Wang, X., Benatallah, B., Sheng, Q.Z.: Expert as a service: software expert recommendation via knowledge domain embeddings in stack overflow. In: Proceedings of the 2017 IEEE International Conference on Web Services, pp. 317–324. IEEE (2017)

    Google Scholar 

  30. Ma, D., Schuler, D., Zimmermann, T., Sillito, J.: Expert recommendation with usage expertise. In: Proceedings of the 2009 IEEE International Conference on Software Maintenance, pp. 535–538. IEEE (2009)

    Google Scholar 

  31. Wang, J., Sun, J., Lin, H., Dong, H., Zhang, S.: Convolutional neural networks for expert recommendation in community question answering. Sci. China: Inf. Sci. 60(11), 19–27 (2017)

    Google Scholar 

  32. Yang, B., Manandhar, S.: Tag-based expert recommendation in community question answering. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 960–963. IEEE (2014)

    Google Scholar 

  33. Hagen, N.T.: Harmonic allocation of authorship credit: Source-level correction of bibliometric bias assures accurate publication and citation analysis. PLoS ONE 3(12), e4021 (2008)

    Article  Google Scholar 

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Correspondence to Xiaolong Xu .

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Xu, X., Yuan, W. (2019). A Network Embedding and Clustering Algorithm for Expert Recommendation Service. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_9

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