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Taherparvar, M., Ahmadi Abkenari, F., Bayat, P.. (1402). Centrality and Latent Semantic Feature Random Walk (CSRW) in Large Network Embedding. فناوری آموزش, 11(2), 311-326. doi: 10.22061/jecei.2023.9279.600
M. Taherparvar; F. Ahmadi Abkenari; P. Bayat. "Centrality and Latent Semantic Feature Random Walk (CSRW) in Large Network Embedding". فناوری آموزش, 11, 2, 1402, 311-326. doi: 10.22061/jecei.2023.9279.600
Taherparvar, M., Ahmadi Abkenari, F., Bayat, P.. (1402). 'Centrality and Latent Semantic Feature Random Walk (CSRW) in Large Network Embedding', فناوری آموزش, 11(2), pp. 311-326. doi: 10.22061/jecei.2023.9279.600
Taherparvar, M., Ahmadi Abkenari, F., Bayat, P.. Centrality and Latent Semantic Feature Random Walk (CSRW) in Large Network Embedding. فناوری آموزش, 1402; 11(2): 311-326. doi: 10.22061/jecei.2023.9279.600

Centrality and Latent Semantic Feature Random Walk (CSRW) in Large Network Embedding

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 11، شماره 2، مهر 2023، صفحه 311-326    اصل مقاله (1.97 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9279.600
نویسندگان
M. Taherparvar1؛ F. Ahmadi Abkenari* 2؛ P. Bayat3
1Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.
2Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran, and Faculty of Computer Engineering and Information Technology, Payam Noor University, Tehran, Iran.
3Department of Computer Engineering, Rasht Branch, Islamic Azad,University, Rasht, Iran.
تاریخ دریافت: 11 مهر 1401،  تاریخ بازنگری: 09 دی 1401،  تاریخ پذیرش: 01 بهمن 1401 
چکیده
Background and Objectives: Embedding social networks has attracted researchers’ attention so far. The aim of network embedding is to learn a low-dimensional representation of each network vertex while maintaining the structure and characteristics of the network. Most of these existing network embedding methods focus on only preserving the structure of networks, but they mostly ignore the semantic and centrality-based information. Moreover, the vertices selection has been done blindly (greedy) in the existing methods.
Methods: In this paper, a comprehensive algorithm entitled CSRW stands for centrality, and a semantic-based random walk is proposed for the network embedding process based on the main criteria of the centrality concept as well as the semantic impact of the textual information of each vertex and considering the impact of neighboring nodes. in CSRW, textual analysis based on the BTM topic modelling approach is investigated and the final display is performed using the Skip-Gram model in the network.
Results: The conducted experiments have shown the robustness of the proposed method of this paper in comparison to other existing classical approaches such as DeepWalk, CARE, CONE, COANE, and DCB in terms of vertex classification, and link prediction. And in the criterion of link prediction in a Subgraph with 5000 members, an accuracy of 0.91 has been reached for the criterion of closeness centrality and is better than other methods.
Conclusion: The CSRW algorithm is scalable and has achieved higher accuracy on larger datasets.
کلیدواژه‌ها
BTM Topic Modelling؛ Centrality Criteria؛ Deep Learning؛ Network Embedding؛ Social Network Analysis
مراجع
[1] B. Perozzi, R. Al-Rfou, S. Skiena, “Deepwalk: Online learning of social representations,” in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 701–710, 2014.

[2] A. Grover, J. Leskovec, “Node2vec: Scalable feature learning for networks,” in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 855–864, 2016.

[3] S. Bhagat, G. Cormode, S. Muthukrishnan, “Node classification in social networks,” Social Network Data Analytics (Springer) Ed. Charu Aggarwal, 2011.

[4] A. Faroughi, R. Javidan, “CANF: Clustering and anomaly detection method using nearest and farthest neighbor,” Future Gener. Comput. Syst., 89: 166–177, 2018.

[5] S. Aslan, M. Kaya, “Topic recommendation for authors as a link prediction problem,” Future Gener. Comput. Syst., 89: 249–264, 2018.

[6] T. Mikolov, K. Chen, G. Corrado, J. Dean, “Efficient estimation of word representations in vector space,” arXiv:1301.3781, 2013.

[7] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, “Line: Large-scale information network embedding,” in Proc. International Conference on World Wide Web: 1067–1077, 2015.

[8] H. Gao, H. Huang, “Deep attributed network embedding,” in Proc. International Joint Conference on Artificial Intelligence: 3364–3370, 2018.

[9] X. Huang, J. Li, X. Hu, “Label informed attributed network embedding,” in Proc. ACM International Conference on Web Search and Data Mining: 731–739, 2017.

[10] J. Butepage, M. J. Black, D. Kragic, H. Kjellstrom, “Deep representation learning for human motion prediction and classification,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition: 1591–1599, 2017.

[11] X. Du, J. J. Y. Wang, “Support image set machine: Jointly learning representation and classifier for image set classification,” Knowledge-Based Syst., 78: 51–58, 2015.

[12] J. Li, J. Li, X. Fu, M. A. Masud, J. Z. Huang, “Learning distributed word representation with multi-contextual mixed embedding,” Knowledge-Based Syst., 106: 220–230, 2016.

[13] M. Janner, K. Narasimhan, R. Barzilay, "Representation learning for grounded spatial reasoning," Trans. Assoc. Computer. Ling., 6: 49–61, 2018.

[14] Z. Chen, T. Cai, C. Chen, Z. Zheng, G. Ling, “SINE: Side information network embedding,” in Proc. International Conference on Database Systems for Advanced Applications: 692–708, 2019.

[15] D. Wang, P. Cui, W. Zhu, “Structural deep network embedding,” in Proc. International Conference on Knowledge Discovery and Data Mining: 1225–1234, 2016.

[16] M. Li, J. Liu, P. Wu, X. Teng, “Evolutionary network embedding preserving both local proximity and community structure,” IEEE Trans. Evol. Comput., 24(3): 523-535, 2019.

[17] J. Chen, Q. Zhang, X. Huang, “Incorporate group information to enhance network embedding,” in Proc. International Conference on Information and Knowledge Management: 1901–1904, 2016.

[18] M. M. Keikha, M. Rahgozar, M. Asadpour, “Community aware random walk for network embedding,” Knowl. Based Syst., 47–54, 2018.

[19] J. Wang, j. Cao, W. Li, S. Wang, “CANE: community-aware network embedding via adversarial training,” Knowl. Inf. Syst., 63: 411-438, 2020.

[20] Y. Shi, M. Lei, H. Yang, L. Niu, “Diffusion network embedding,” Pattern Recognit., 88: 518–531, 2019.

[21] H. Chen, H. Yin, T. Chen, Q.V.H. Nguyen, W.-C. Peng, X. Li, “Exploiting centrality information with graph convolutions for network representation learning,” in Proc. IEEE International Conference on Data Engineering: 590–601, 2019.

[22] W. Zhao, H. Ma, Z. Li, X. Ao, N. Li, “SBRNE: An improved unified framework for social and behavior recommendations with network embedding,” in Proc. International Conference on Database Systems for Advanced Applications: 555–571, 2019.

[23] Q. Li, J. Zhong, Q. Li, Z. Cao, C. Wang, “Enhancing network embedding with implicit clustering,” in Proc. International Conference on Database Systems for Advanced Applications: 452–467, 2019.

[24] M. RosenZvi, T. Griffiths, M. Steyvers, P. Smyth, “The author-topic model for authors and documents,” in Proc. Uncertainty in Artificial Intelligence: 487–494, 2004.

[25] Q. Mei, D. Cai, D. Zhang, C. Zhai, “Topic modeling with network regularization,” in Proc: International Conference on World Wide Web: 101–110, 2008.

[26] L. Wu, D. Wang, S. Feng, Y. Zhang, G. Yu, MDAL: “Multi-task Dual Attention LSTM Model for Semi-supervised Network Embedding,” in Proc: International Conference on Database Systems for Advanced Applications: 468–483, 2019.

[27] Y. Gao, M. Gong, Y. Xie, H. Zhong, “Community-oriented attributed network embedding,” Knowledge-Based Systems, 193: 105418, 2019.

[28] M. Taherparvar, F. Ahmadi Abkenari, P. Bayat, “Attribute network embedding based on maintaining the structure and semantic features of the graphs,” in Proc: International Conference on The New Horizons in The Electrical Engineering, Computer and Mechanical, 2022.

[29] L. Chen, Y. Li, X. Deng, Z. Liu, M. Lv, T. He, “Semantic-aware network embedding via optimized random walk and paragaraph2vec,” J. Comput. Sci., 63: 101825, 2022.

[30] J. Zhang, Yu. Luo, “Degree centrality, betweenness centrality, and closeness centrality in social network,” in proc. International Conference on Modelling, Simulation and Applied Mathematics, 2017.

[31] P. Bonacich, “Some unique properties of eigenvector centrality,” Social Network, 29(4): 555-564, 2007.

[32] L. Maccari, L. Ghiro, A. Guerrieri, A. Montresor, R. Lo Cigno, “On the distributed computation of load centrality and its application to DV routing,” in proc. IEEE Conference on Computer Communications, 2018.

[33] M. Taherparvar, F. Ahmadi Abkenari, P. Bayat, “Conformance evaluation of topic modeling approaches on web-based short text dynamic graph databases,” in proc. International Conference on Web Research, 2021.

[34] J. Tang, Z. Meng, X. Nguyen, Q. Mei, M. Zhang, “Understanding the limiting factors of topic modeling via posterior contraction analysis,” in Proc. International Conference on Machine Learning: 190–198, 2014.

[35] A. K. McCallum, K. Nigam, J. Rennie, K. Seymore, “Automating the construction of internet portals with machine learning,” Information Retrieval: 127–163, 2000.

[36] https://www.aminer.org/citation, last access June 17, 2023.

 

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