تشخیص اجتماع در شبکه های اجتماعی با رویکرد یادگیری عمیق

References

Al-Andoli, M., Cheah, W. P., & Tan, S. C. (2021). Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters. Journal of Ambient Intelligence and Humanized Computing, 12 (2), 2527-2545. ‏

Al-Andoli, M. N., Tan, S. C., Cheah, W. P., & Tan, S. Y. (2021). A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms. IEEE Access, 9, 96501-96527.‏

Bhatia, V., & Rani, R. (2019). A distributed overlapping community detection model for large graphs using autoencoder. Future Generation Computer Systems, 94, 16-26. ‏

Berahmand, K., Bouyer, A., & Vasighi, M. (2018). Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Transactions on Computational Social Systems, 5(4), 1021-1033.‏

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008 (10), P10008. ‏

Cai, B., Wang, Y., Zeng, L., Hu, Y., & Li, H. (2020). Edge classification based on convolutional neural networks for community detection in complex network. Physica A: Statistical Mechanics and its Applications, 556, 124826. ‏

Cao, J., Jin, D., & Dang, J. (2018, August). Autoencoder based community detection with adaptive integration of network topology and node contents. In International conference on knowledge science, engineering and management (pp. 184-196). Springer, Cham. ‏

Cao, J., Jin, D., Yang, L., & Dang, J. (2018). Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing, 297, 71-81. ‏

Chen, Z., Li, X., & Bruna, J. (2019). Supervised community detection with line graph neural networks. arXiv preprint arXiv:1705. 08415. ‏

Choong, J. J., Liu, X., & Murata, T. (2019, December). Optimizing variational graph autoencoder for community detection. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5353-5358). IEEE. ‏

Dhilber, M., & Bhavani, S. D. (2020, January). Community detection in social networks using deep learning. In International conference on distributed computing and internet technology (pp. 241-250). Springer, Cham. ‏

Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical review E, 72(2), 027104.‏

Fei, R., Sha, J., Xu, Q., Hu, B., Wang, K., & Li, S. (2020). A new deep sparse autoencoder for community detection in complex networks. EURASIP Journal on Wireless Communications and Networking, 2020 (1), 1-25.

Geng, X., Lu, H., & Sun, J. (2020). Network structural transformation-based community detection with autoencoder. Symmetry, 12 (6), 944. ‏

Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12), 7821-7826.‏

Guo, K., Zhang, P., Guo, W., & Chen, Y. (2022). An attentional-walk-based autoencoder for community detection. Applied Intelligence, 1-19.‏

He, D., Feng, Z., Jin, D., Wang, X., & Zhang, W. (2017, February). Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In Thirty-First AAAI Conference on Artificial Intelligence. ‏

He, D., Song, Y., Jin, D., Feng, Z., Zhang, B., Yu, Z., & Zhang, W. (2021, January). Community-centric graph convolutional network for unsupervised community detection. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 3515-3521). ‏

Ivannikova, E., Park, H., Hämäläinen, T., & Lee, K. (2018). Revealing community structures by ensemble clustering using group diffusion. Information Fusion, 42, 24-36.‏

Jia, Y., Zhang, Q., Zhang, W., & ang, X. (2019, May). Communitygan: Community detection with generative adversarial nets. In The World Wide Web Conference (pp. 784-794). ‏

Jin, D., Gabrys, B., & Dang, J. (2015). Combined node and link partitions method for finding overlapping communities in complex networks. Scientific reports, 5 (1), 1-8. ‏

Jin, D., Ge, M., Li, Z., Lu, W., He, D., & Fogelman-Soulie, F. (2017, November). Using deep learning for community discovery in social networks. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 160-167). IEEE. ‏

Jin, D., Liu, Z., Li, W., He, D., & Zhang, W. (2019, July). Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 152-159).

Kanawati, R. (2014, August). YASCA: an ensemble-based approach for community detection in complex networks. In International Computing and Combinatorics Conference (pp. 657-666). Springer, Cham. ‏

Li, Y., Han, Q., & Liu, J. (2019, November). Community Detection based on Autoencoder Reconstruction Similarity Matrix. In Journal of Physics: Conference Series (Vol. 1345, No. 3, p. 032055). IOP Publishing. ‏

Liu, C., Liu, J., & Jiang, Z. (2014). A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE transactions on cybernetics, 44 (12), 2274-2287. ‏

Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C.,... & Yu, P. S. (2020). Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005. 08225.

Liu, R., Wang, H., & Yu, X. (2018). Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Information Sciences, 450, 200-226.‏

‏Liu, J. (2010, October). Comparative analysis for k-means algorithms in network community detection. In International Symposium on Intelligence Computation and Applications (pp. 158-169). Springer, Berlin, Heidelberg.‏

Lusseau, D., & Newman, M. E. (2004). Identifying the role that animals play in their social networks. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271 (suppl_6), S477-S481. ‏

Mahmood, A., & Small, M. (2015). Subspace based network community detection using sparse linear coding. IEEE Transactions on Knowledge and Data Engineering, 28 (3), 801-812. ‏

Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69 (6), 066133. ‏

Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.‏

Salehi, S. M., & Pouyan, A. A. (2020). Detecting overlapping communities in social networks using deep learning. International Journal of Engineering, 33 (3), 366-376. ‏

Shchur, O., & Günnemann, S. (2019). Overlapping community detection with graph neural networks. arXiv preprint arXiv:1909. 12201. ‏

Souravlas, S., Anastasiadou, S., & Katsavounis, S. (2021). A Survey on the Recent Advances of Deep Community Detection. Applied Sciences, 11 (16), 7179. ‏

Sperlí, G. (2019, April). A deep learning based community detection approach. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 1107-1110). ‏

Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C.,... & Yu, P. S. (2021). A Comprehensive Survey on Community Detection with Deep Learning. arXiv preprint arXiv:2105. 12584. ‏

Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T. Y. (2014, June). Learning deep representations for graph clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 28, No. 1). ‏

Wang, F., Zhang, B., Chai, S., & Xia, Y. (2018). Community detection in complex networks using deep auto-encoded extreme learning machine. Modern Physics Letters B, 32 (16), 1850180. ‏

Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., & Zhang, C. (2019). Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906. 06532. ‏

Wang, F., Zhang, B., & Chai, S. (2019). Deep auto-encoded clustering algorithm for community detection in complex networks. Chinese Journal of Electronics, 28 (3), 489-496. ‏

Wu, L., Zhang, Q., Chen, C. H., Guo, K., & Wang, D. (2020). Deep learning techniques for community detection in social networks. IEEE Access, 8, 96016-96026. ‏

Xie, Y., Gong, M., Wang, S., & Yu, B. (2018). Community discovery in networks with deep sparse filtering. Pattern Recognition, 81, 50-59. ‏

Xie, Y., Wang, X., Jiang, D., & Xu, R. (2019). High-performance community detection in social networks using a deep transitive autoencoder. Information Sciences, 493, 75-90. ‏

Xin, X., Wang, C., Ying, X., & Wang, B. (2017). Deep community detection in topologically incomplete networks. Physica A: Statistical Mechanics and its Applications, 469, 342-352. ‏

Xu, R., Che, Y., Wang, X., Hu, J., & Xie, Y. (2020). Stacked autoencoder-based community detection method via an ensemble clustering framework. Information sciences, 526, 151-165. ‏

Yang, J., McAuley, J., & Leskovec, J. (2013, December). Community detection in networks with node attributes. In 2013 IEEE 13th international conference on data mining (pp. 1151-1156). IEEE. ‏

Yang, L., Cao, X., He, D., Wang, C., Wang, X., & Zhang, W. (2016, July). Modularity Based Community Detection with Deep Learning. In IJCAI (Vol. 16, pp. 2252-2258). ‏

Yang, T., Jin, R., Chi, Y., & Zhu, S. (2009, June). Combining link and content for community detection: a discriminative approach. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 927-936).‏

Ye, F., Chen, C., & Zheng, Z. (2018, October). Deep autoencoder-like nonnegative matrix factorization for community detection. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 1393-1402). ‏

Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of anthropological research, 33 (4), 452-473.

Zhang, Y., Xiong, Y., Ye, Y., Liu, T., Wang, W., Zhu, Y., & Yu, P. S. (2020, August). SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1103-1113). ‏

References [In Persian]

Ghasempoor, M., Seifi, A., & Alizadeh, H. (2019). Recognition of communities in social networks. The second national industrial and systems engineering conference, Isfahan, https://civilica. com/doc/251290

Hosseinian, A. H., Teimourpour, B., & Jamali Hondori, B. (2019). A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion. Business Intelligence Management Studies, 8(29), 61-86. doi: 10.22054/ims.2019.10376

Kosari Langari, R., Sardar, S., Amin Mousavi, S. A., & Radfar, R. (2019). A Model to Publish Online Social Networks Data with Privacy Preserving. Business Intelligence Management Studies, 8(29), 87-112. doi: 10.22054/ims.2019.10377

Rohan, S., Amirian, S., & Mohammadian, A. (2017). Identification and Ranking of Social Mining Applications in E-Commerce. Business Intelligence Management Studies, 6(21), 1-32. doi: 10.22054/ims.2018.8510

Roshani, S., Rezaeenik, N., & Shojaei, S. M. H. (2013). A comparative study of Usability and Sociability Of public and specialized social networking websites. Business Intelligence Management Studies, 2(5), 97-132.

Shoar, M., & Salarnezhad, A. A. (2018). A New Method to Cluster HTML Documents Using Mixed Algorithms. Business Intelligence Management Studies, 6(24), 37-62. doi: 10.22054/ims.2018.8891

 

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‌ ‌استناد به این مقاله: حسینی، منیره.، گلوی، الناز. (1402). تشخیص اجتماع در شبکه‌های اجتماعی با رویکرد یادگیری عمیق، مطالعات مدیریت کسب وکار هوشمند، 11(44)، 83-112.

DOI: 10.22054/ims.2023.66048.2125

 Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..

References

Al-Andoli, M., Cheah, W. P., & Tan, S. C. (2021). Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters. Journal of Ambient Intelligence and Humanized Computing, 12 (2), 2527-2545. ‏

Al-Andoli, M. N., Tan, S. C., Cheah, W. P., & Tan, S. Y. (2021). A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms. IEEE Access, 9, 96501-96527.‏

Bhatia, V., & Rani, R. (2019). A distributed overlapping community detection model for large graphs using autoencoder. Future Generation Computer Systems, 94, 16-26. ‏

Berahmand, K., Bouyer, A., & Vasighi, M. (2018). Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Transactions on Computational Social Systems, 5(4), 1021-1033.‏

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008 (10), P10008. ‏

Cai, B., Wang, Y., Zeng, L., Hu, Y., & Li, H. (2020). Edge classification based on convolutional neural networks for community detection in complex network. Physica A: Statistical Mechanics and its Applications, 556, 124826. ‏

Cao, J., Jin, D., & Dang, J. (2018, August). Autoencoder based community detection with adaptive integration of network topology and node contents. In International conference on knowledge science, engineering and management (pp. 184-196). Springer, Cham. ‏

Cao, J., Jin, D., Yang, L., & Dang, J. (2018). Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing, 297, 71-81. ‏

Chen, Z., Li, X., & Bruna, J. (2019). Supervised community detection with line graph neural networks. arXiv preprint arXiv:1705. 08415. ‏

Choong, J. J., Liu, X., & Murata, T. (2019, December). Optimizing variational graph autoencoder for community detection. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5353-5358). IEEE. ‏

Dhilber, M., & Bhavani, S. D. (2020, January). Community detection in social networks using deep learning. In International conference on distributed computing and internet technology (pp. 241-250). Springer, Cham. ‏

Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical review E, 72(2), 027104.‏

Fei, R., Sha, J., Xu, Q., Hu, B., Wang, K., & Li, S. (2020). A new deep sparse autoencoder for community detection in complex networks. EURASIP Journal on Wireless Communications and Networking, 2020 (1), 1-25.

Geng, X., Lu, H., & Sun, J. (2020). Network structural transformation-based community detection with autoencoder. Symmetry, 12 (6), 944. ‏

Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12), 7821-7826.‏

Guo, K., Zhang, P., Guo, W., & Chen, Y. (2022). An attentional-walk-based autoencoder for community detection. Applied Intelligence, 1-19.‏

He, D., Feng, Z., Jin, D., Wang, X., & Zhang, W. (2017, February). Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In Thirty-First AAAI Conference on Artificial Intelligence. ‏

He, D., Song, Y., Jin, D., Feng, Z., Zhang, B., Yu, Z., & Zhang, W. (2021, January). Community-centric graph convolutional network for unsupervised community detection. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 3515-3521). ‏

Ivannikova, E., Park, H., Hämäläinen, T., & Lee, K. (2018). Revealing community structures by ensemble clustering using group diffusion. Information Fusion, 42, 24-36.‏

Jia, Y., Zhang, Q., Zhang, W., & ang, X. (2019, May). Communitygan: Community detection with generative adversarial nets. In The World Wide Web Conference (pp. 784-794). ‏

Jin, D., Gabrys, B., & Dang, J. (2015). Combined node and link partitions method for finding overlapping communities in complex networks. Scientific reports, 5 (1), 1-8. ‏

Jin, D., Ge, M., Li, Z., Lu, W., He, D., & Fogelman-Soulie, F. (2017, November). Using deep learning for community discovery in social networks. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 160-167). IEEE. ‏

Jin, D., Liu, Z., Li, W., He, D., & Zhang, W. (2019, July). Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 152-159).

Kanawati, R. (2014, August). YASCA: an ensemble-based approach for community detection in complex networks. In International Computing and Combinatorics Conference (pp. 657-666). Springer, Cham. ‏

Li, Y., Han, Q., & Liu, J. (2019, November). Community Detection based on Autoencoder Reconstruction Similarity Matrix. In Journal of Physics: Conference Series (Vol. 1345, No. 3, p. 032055). IOP Publishing. ‏

Liu, C., Liu, J., & Jiang, Z. (2014). A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE transactions on cybernetics, 44 (12), 2274-2287. ‏

Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C.,... & Yu, P. S. (2020). Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005. 08225.

Liu, R., Wang, H., & Yu, X. (2018). Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Information Sciences, 450, 200-226.‏

‏Liu, J. (2010, October). Comparative analysis for k-means algorithms in network community detection. In International Symposium on Intelligence Computation and Applications (pp. 158-169). Springer, Berlin, Heidelberg.‏

Lusseau, D., & Newman, M. E. (2004). Identifying the role that animals play in their social networks. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271 (suppl_6), S477-S481. ‏

Mahmood, A., & Small, M. (2015). Subspace based network community detection using sparse linear coding. IEEE Transactions on Knowledge and Data Engineering, 28 (3), 801-812. ‏

Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69 (6), 066133. ‏

Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.‏

Salehi, S. M., & Pouyan, A. A. (2020). Detecting overlapping communities in social networks using deep learning. International Journal of Engineering, 33 (3), 366-376. ‏

Shchur, O., & Günnemann, S. (2019). Overlapping community detection with graph neural networks. arXiv preprint arXiv:1909. 12201. ‏

Souravlas, S., Anastasiadou, S., & Katsavounis, S. (2021). A Survey on the Recent Advances of Deep Community Detection. Applied Sciences, 11 (16), 7179. ‏

Sperlí, G. (2019, April). A deep learning based community detection approach. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 1107-1110). ‏

Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C.,... & Yu, P. S. (2021). A Comprehensive Survey on Community Detection with Deep Learning. arXiv preprint arXiv:2105. 12584. ‏

Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T. Y. (2014, June). Learning deep representations for graph clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 28, No. 1). ‏

Wang, F., Zhang, B., Chai, S., & Xia, Y. (2018). Community detection in complex networks using deep auto-encoded extreme learning machine. Modern Physics Letters B, 32 (16), 1850180. ‏

Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., & Zhang, C. (2019). Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906. 06532. ‏

Wang, F., Zhang, B., & Chai, S. (2019). Deep auto-encoded clustering algorithm for community detection in complex networks. Chinese Journal of Electronics, 28 (3), 489-496. ‏

Wu, L., Zhang, Q., Chen, C. H., Guo, K., & Wang, D. (2020). Deep learning techniques for community detection in social networks. IEEE Access, 8, 96016-96026. ‏

Xie, Y., Gong, M., Wang, S., & Yu, B. (2018). Community discovery in networks with deep sparse filtering. Pattern Recognition, 81, 50-59. ‏

Xie, Y., Wang, X., Jiang, D., & Xu, R. (2019). High-performance community detection in social networks using a deep transitive autoencoder. Information Sciences, 493, 75-90. ‏

Xin, X., Wang, C., Ying, X., & Wang, B. (2017). Deep community detection in topologically incomplete networks. Physica A: Statistical Mechanics and its Applications, 469, 342-352. ‏

Xu, R., Che, Y., Wang, X., Hu, J., & Xie, Y. (2020). Stacked autoencoder-based community detection method via an ensemble clustering framework. Information sciences, 526, 151-165. ‏

Yang, J., McAuley, J., & Leskovec, J. (2013, December). Community detection in networks with node attributes. In 2013 IEEE 13th international conference on data mining (pp. 1151-1156). IEEE. ‏

Yang, L., Cao, X., He, D., Wang, C., Wang, X., & Zhang, W. (2016, July). Modularity Based Community Detection with Deep Learning. In IJCAI (Vol. 16, pp. 2252-2258). ‏

Yang, T., Jin, R., Chi, Y., & Zhu, S. (2009, June). Combining link and content for community detection: a discriminative approach. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 927-936).‏

Ye, F., Chen, C., & Zheng, Z. (2018, October). Deep autoencoder-like nonnegative matrix factorization for community detection. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 1393-1402). ‏

Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of anthropological research, 33 (4), 452-473.

Zhang, Y., Xiong, Y., Ye, Y., Liu, T., Wang, W., Zhu, Y., & Yu, P. S. (2020, August). SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1103-1113). ‏

References [In Persian]

Ghasempoor, M., Seifi, A., & Alizadeh, H. (2019). Recognition of communities in social networks. The second national industrial and systems engineering conference, Isfahan, https://civilica. com/doc/251290

Hosseinian, A. H., Teimourpour, B., & Jamali Hondori, B. (2019). A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion. Business Intelligence Management Studies, 8(29), 61-86. doi: 10.22054/ims.2019.10376

Kosari Langari, R., Sardar, S., Amin Mousavi, S. A., & Radfar, R. (2019). A Model to Publish Online Social Networks Data with Privacy Preserving. Business Intelligence Management Studies, 8(29), 87-112. doi: 10.22054/ims.2019.10377

Rohan, S., Amirian, S., & Mohammadian, A. (2017). Identification and Ranking of Social Mining Applications in E-Commerce. Business Intelligence Management Studies, 6(21), 1-32. doi: 10.22054/ims.2018.8510

Roshani, S., Rezaeenik, N., & Shojaei, S. M. H. (2013). A comparative study of Usability and Sociability Of public and specialized social networking websites. Business Intelligence Management Studies, 2(5), 97-132.

Shoar, M., & Salarnezhad, A. A. (2018). A New Method to Cluster HTML Documents Using Mixed Algorithms. Business Intelligence Management Studies, 6(24), 37-62. doi: 10.22054/ims.2018.8891

 

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