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姓名	凃家甄(Chia-Chen Tu)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	基於社群網路分析的模式增強轉換網路 (A Novel Social Network Prediction with Augmented Pattern-Based Transformer)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-7-1以後開放)
摘要(中)	群體意識，是刻在血液裡的記憶。當古人由獨居走向部落，人際關係的概念在交流中形成，並藉由每一次的相互影響，逐漸深化。關係鏈的交織，讓獨立的小團體們發展成難以切割的社群網路。隨著網際網路的發展，社群網站上的分享逐漸取代了現實生活中的交流。倘若人們不再透過見面聯繫感情、認識新朋友不再僅限於線下活動，當人際互動逐漸由實體轉向虛擬，如果我們試圖了解一個人的行為模式與偏好，則應聚焦在社群平台上的社群網路分析。社群網路分析的首要目標，在於透過所有使用者在人際網路中的互動關係，識別出目標使用者在此研究範圍下的潛在關係，而其最常見的應用方法為連結預測。此外，由於關係網路會隨著時間演化，當我們在實踐連結預測時，應考慮其動態性，即動態連結預測。在過去的研究中，時間範圍的切割造成資訊量不足、圖像資料導致運算過於複雜，以及無法兼顧個人與整體關係網路資訊等問題，造成最終的預測效果不彰。基於以上的挑戰，本研究提出了以特徵進行分析的增強式轉換網路架構——NET-APT，以改善已知的分析劣勢，提高社群網路關係預測的成效。同時，我們也將NET-APT運用至真實世界的資料集中，並驗證其表現皆優於基準模型。對於此研究的未來展望，我們認為可以將NET-APT應用至商業領域，以其即時性的優勢結合基於特徵的分析模式，根據使用者當下的行為特質，向其發送可能會感興趣的文章乃至廣告、社團或粉絲專頁建議。
摘要(英)	Throughout history, human beings′ social nature has fostered the development and deepening of interpersonal relationships through interactions and collaborations. With the advent of the Internet, people increasingly rely on virtual interactions and relationships, there is a need to shift our focus to social network analysis on social media platforms in order to uncover personal interaction patterns and preferences. The primary objective of social network analysis is to identify latent associations and connections within a social network, particularly with respect to a target user. This is commonly achieved through dynamic network link prediction techniques. However, past researches have faced challenges in achieving accurate predictions due to the lack of information caused by temporal splits, the computational complexity associated with graph data, and the failure to capture both personal and general network information. To address these challenges, we propose NET-APT, an augmented pattern-based Transformer architecture designed to enhance the effectiveness of social network prediction. We evaluate the performance of NET-APT using real-world datasets and compare it against baseline models, demonstrating its superior predictive capabilities. In terms of future development, we envision the application of NET-APT in commercial domains. For example, it could be utilized to send personalized advertisements based on users′ interaction patterns, enabling real-time pattern-based analysis. This potential application highlights the value of NET-APT in enhancing targeted marketing and providing tailored suggestions to users in various contexts.
關鍵字(中)	★ 社群網路分析 ★ 動態連結預測 ★ 基於特徵的轉換網路	關鍵字(英)	★ Social Network Analysis ★ Dynamic Network Link Prediction ★ Pattern-Based Transformer
論文目次	摘 要 i Abstract ii 誌 謝 iii Table of Contents iv List of Figures v List of Tables vi I. Introduction 1 II. Related Work 6 2.1 Social Network Analysis 6 2.2 Link Prediction 7 2.2.1 Static Network Link Prediction 8 2.2.2 Dynamic Network Link Prediction 9 2.3 Augmentation 11 III. Proposed Method 12 3.1 Sequence Transformation 15 3.2 Augmentation 16 3.3 Pattern Discovery 19 3.4 Evolution Learning 21 IV. Experiments and Evaluation 28 4.1 Evaluation Metric and Baseline Models 30 4.2 Performance Comparison 32 4.3 Minimum Support Setting Analysis 35 4.4 Ablation Study 36 4.5 Parameter Setting 39 4.5.1 Epoch 39 4.5.2 Dropout Rate 40 4.5.3 Batch Size 42 4.6 Case Study 44 V. Conclusion 46 Reference 47
參考文獻	[1] S. P. Borgatti, A. Mehra, D. J. Brass, and G. Labianca, "Network analysis in the social sciences," science, vol. 323, no. 5916, pp. 892-895, 2009. [2] J. Wang and J. Han, "BIDE: Efficient mining of frequent closed sequences," in Proceedings. 20th international conference on data engineering, 2004: IEEE, pp. 79-90. [3] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017. [4] M. Udrescu and A. Topirceanu, "Probabilistic modeling of tolerance-based social network interaction," in 2016 Third European Network Intelligence Conference (ENIC), 2016: IEEE, pp. 48-54. [5] J. Li, Y. Gao, X. Gao, Y. Shi, and G. Chen, "SENTI2POP: sentiment-aware topic popularity prediction on social media," in 2019 IEEE International conference on data mining (ICDM), 2019: IEEE, pp. 1174-1179. [6] S. Yum, "Social network analysis for coronavirus (COVID‐19) in the United States," Social Science Quarterly, vol. 101, no. 4, pp. 1642-1647, 2020. [7] D. Nurbakova, L. Ermakova, and I. Ovchinnikova, "Understanding the Personality of Contributors to Information Cascades in Social Media in response to the COVID-19 Pandemic," in 2020 International Conference on Data Mining Workshops (ICDMW), 2020: IEEE, pp. 45-52. [8] D. Dimitrov, E. Baran, P. Fafalios, R. Yu, X. Zhu, M. Zloch, and S. Dietze, "Tweetscov19-a knowledge base of semantically annotated tweets about the covid-19 pandemic," in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, pp. 2991-2998. [9] Y. Ye, H. Zhu, T. Xu, F. Zhuang, R. Yu, and H. Xiong, "Identifying high potential talent: A neural network based dynamic social profiling approach," in 2019 IEEE International Conference on Data Mining (ICDM), 2019: IEEE, pp. 718-727. [10] M. Grohe, "word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data," in Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2020, pp. 1-16. [11] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, "Line: Large-scale information network embedding," in Proceedings of the 24th international conference on world wide web, 2015, pp. 1067-1077. [12] D. Wang, P. Cui, and W. Zhu, "Structural deep network embedding," in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 1225-1234. [13] L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo, "struc2vec: Learning node representations from structural identity," in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017, pp. 385-394. [14] Z. Yang, Y. Zhang, and Y. Dai, "Defending against social network sybils with interaction graph embedding," in 2018 IEEE Conference on Communications and Network Security (CNS), 2018: IEEE, pp. 1-9. [15] J. Chen, J. Zhang, Z. Chen, M. Du, and Q. Xuan, "Time-aware gradient attack on dynamic network link prediction," IEEE Transactions on Knowledge and Data Engineering, 2021. [16] X. Li, Y. Li, Y. Shang, L. Tong, F. Fang, P. Yin, J. Cheng, and J. Li, "Ddne: Discriminative distance metric learning for network embedding," in Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part I 20, 2020: Springer, pp. 568-581.   [17] L. Wang, J. Ren, B. Xu, J. Li, W. Luo, and F. Xia, "Model: Motif-based deep feature learning for link prediction," IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 503-516, 2020. [18] Z. Liao, L. Liu, and Y. Chen, "A novel link prediction method for opportunistic networks based on random walk and a deep belief network," Ieee Access, vol. 8, pp. 16236-16247, 2020. [19] W. Radstok, M. Chekol, and Y. Velegrakis, "Leveraging static models for link prediction in temporal knowledge graphs," in 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021: IEEE, pp. 1034-1041. [20] P. Pho and A. V. Mantzaris, "Link prediction with Simple Graph Convolution and regularized Simple Graph Convolution," in Proceedings of the 6th International Conference on Information System and Data Mining, 2022, pp. 31-35. [21] N. Kuang, Y. Zuo, Y. Huo, L. Jiao, X. Gong, and Y. Yang, "Network Link Connectivity Prediction Based on GCN and Differentiable Pooling Model," in 2022 IEEE 14th International Conference on Advanced Infocomm Technology (ICAIT), 2022: IEEE, pp. 1-6. [22] Y. Dhote, N. Mishra, and S. Sharma, "Survey and analysis of temporal link prediction in online social networks," in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013: IEEE, pp. 1178-1183. [23] X. Wu, J. Wu, Y. Li, and Q. Zhang, "Link prediction of time-evolving network based on node ranking," Knowledge-Based Systems, vol. 195, p. 105740, 2020. [24] Y.-C. Chen, Y.-L. Chen, and J.-Y. Lu, "MK-Means: Detecting evolutionary communities in dynamic networks," Expert Systems with Applications, vol. 176, p. 114807, 2021. [25] A. Alamsyah, M. K. Bratawisnu, and P. H. Sanjani, "Finding pattern in dynamic network analysis," in 2018 6th International Conference on Information and Communication Technology (ICoICT), 2018: IEEE, pp. 141-146. [26] M. F. Firdaus, Z. Baizal, M. K. Bratawisnu, and H. A. Gusman, "Pattern Recognition of Dynamic Social Network," in 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS), 2020: IEEE, pp. 56-61. [27] X. Li, W. Liang, X. Zhang, X. Liu, and W. Wu, "A universal method based on structure subgraph feature for link prediction over dynamic networks," in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019: IEEE, pp. 1210-1220. [28] A. Sharma, S. Soni, and K. Rai, "Link Prediction in Social Network using Artificial Neural Network," Int. J. Comput. Appl, vol. 174, pp. 26-30, 2021. [29] K. Selvarajah, K. Ragunathan, Z. Kobti, and M. Kargar, "Dynamic network link prediction by learning effective subgraphs using CNN-LSTM," in 2020 International Joint Conference on Neural Networks (IJCNN), 2020: IEEE, pp. 1-8. [30] J. Skarding, M. Hellmich, B. Gabrys, and K. Musial, "A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction," IEEE Access, vol. 10, pp. 64146-64160, 2022. [31] L. Wang, X. Chang, S. Li, Y. Chu, H. Li, W. Zhang, X. He, L. Song, J. Zhou, and H. Yang, "Tcl: Transformer-based dynamic graph modelling via contrastive learning," arXiv preprint arXiv:2105.07944, 2021. [32] A. Sankar, Y. Wu, L. Gou, W. Zhang, and H. Yang, "Dysat: Deep neural representation learning on dynamic graphs via cross networks," in Proceedings of the 13th international conference on web search and data mining, 2020, pp. 519-527. [33] B. Knyazev, C. Augusta, and G. W. Taylor, "Learning temporal attention in dynamic graphs with bilinear interactions," Plos one, vol. 16, no. 3, p. e0247936, 2021. [34] H. Xue, L. Yang, W. Jiang, Y. Wei, Y. Hu, and Y. Lin, "Modeling dynamic heterogeneous network for link prediction using hierarchical attention with temporal rnn," in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I, 2021: Springer, pp. 282-298. [35] L. Liu, Y. Zhang, S. Fu, F. Zhong, J. Hu, and P. Zhang, "ABNE: an attention-based network embedding for user alignment across social networks," IEEE Access, vol. 7, pp. 23595-23605, 2019. [36] H. Shao, L. Wang, and Y. Ji, "Link prediction algorithms for social networks based on machine learning and HARP," IEEE Access, vol. 7, pp. 122722-122729, 2019. [37] R. Trivedi, M. Farajtabar, P. Biswal, and H. Zha, "Dyrep: Learning representations over dynamic graphs," in International conference on learning representations, 2019. [38] D. Zou and G. Lerman, "Encoding robust representation for graph generation," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019: IEEE, pp. 1-9. [39] K. Lei, M. Qin, B. Bai, G. Zhang, and M. Yang, "GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks," in IEEE INFOCOM 2019-IEEE conference on computer communications, 2019: IEEE, pp. 388-396. [40] K. P. Kumar and M. L. Gavrilova, "User Identification in Online Social Networks using Graph Transformer Networks," in 2021 18th International Conference on Privacy, Security and Trust (PST), 2021: IEEE, pp. 1-10. [41] J. Luo, M. Bouazizi, and T. Ohtsuki, "Data augmentation for sentiment analysis using sentence compression-based Seqgan with data screening," IEEE Access, vol. 9, pp. 99922-99931, 2021. [42] H. Bo, R. McConville, J. Hong, and W. Liu, "Social influence prediction with train and test time augmentation for graph neural networks," in 2021 International Joint Conference on Neural Networks (IJCNN), 2021: IEEE, pp. 1-8. [43] F. Long, N. Ning, C. Song, and B. Wu, "Strengthening social networks analysis by networks fusion," in Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 460-463. [44] Y. Wang, C. Piao, C. H. Liu, C. Zhou, and J. Tang, "Modeling User Interests With Online Social Network Influence by Memory Augmented Sequence Learning," IEEE Transactions on Network Science and Engineering, vol. 8, no. 1, pp. 541-554, 2020. [45] F. A. Aagesen, B. E. Helvik, C. Anutariya, and M. M. Shiaa, "On adaptable networking," in Proceedings of the First International Conference on Information and Communication Technologies, ICT’2003, 2003. [46] Z. Liu, Z. Fan, Y. Wang, and P. S. Yu, "Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer," in Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval, 2021, pp. 1608-1612. [47] W.-C. Kang and J. McAuley, "Self-attentive sequential recommendation," in 2018 IEEE international conference on data mining (ICDM), 2018: IEEE, pp. 197-206. [48] R. Michalski, S. Palus, and P. Kazienko, "Matching organizational structure and social network extracted from email communication," in Business Information Systems: 14th International Conference, BIS 2011, Poznań, Poland, June 15-17, 2011. Proceedings 14, 2011: Springer, pp. 197-206. [49] R. Rossi and N. Ahmed, "The network data repository with interactive graph analytics and visualization," in Proceedings of the AAAI conference on artificial intelligence, 2015, vol. 29, no. 1. [50] G.-Y. Jheng, Y.-C. Chen, and H.-M. Liang, "Evolution pattern mining on dynamic social network," The Journal of Supercomputing, vol. 77, pp. 6979-6991, 2021. [51] B. Perozzi, R. Al-Rfou, and S. Skiena, "Deepwalk: Online learning of social representations," in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701-710. [52] A. Grover and J. Leskovec, "node2vec: Scalable feature learning for networks," in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855-864. [53] J. Chen, J. Zhang, X. Xu, C. Fu, D. Zhang, Q. Zhang, and Q. Xuan, "E-LSTM-D: A deep learning framework for dynamic network link prediction," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3699-3712, 2019. [54] T. Li, J. Zhang, S. Y. Philip, Y. Zhang, and Y. Yan, "Deep dynamic network embedding for link prediction," IEEE Access, vol. 6, pp. 29219-29230, 2018. [55] J. Chen, X. Wang, and X. Xu, "GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction," Applied Intelligence, pp. 1-16, 2022. [56] Q. Wang, W. Zhao, J. Yang, J. Wu, C. Zhou, and Q. Xing, "Atne-trust: Attributed trust network embedding for trust prediction in online social networks," in 2020 IEEE International Conference on Data Mining (ICDM), 2020: IEEE, pp. 601-610.
指導教授	陳以錚(Yi-Cheng Chen)	審核日期	2023-7-24
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