社群網路是近幾年十分蓬勃發展的一個科技，隨著他蓬勃發展，各式各樣預測社群網路的方法因應而生，這些方法大多是使用靜態社群網路來做預測，然而現實中的社群網路則大多是動態改變的，本研究以Mobile01論壇的資料為資料庫，利用EPMiner資料探勘演算法從數據中找出頻繁序列，利用長短期記憶（LSTM）模型預測社群網路關係。本研究提出了一個具有三個 LSTM 的兩層架構模型來進行預測。

Social networking is a technology that has been booming in recent years. With its vigorous development, various methods of predicting social networks have emerged. Most of these methods use static social networks to make predictions. However, social networks in reality are mostly dynamically changing. This research uses the data of the Mobile01 forum as the database, uses the EPMiner data mining algorithm to find frequent sequences from the data, and uses the long short-term memory (LSTM) model to predict social network relationships. This research proposes a two-tier architecture model with three-LSTM to make predictions.

摘要：	I
英文摘要：	II
目錄	IV
第一章 緒論	1
第二章 相關文獻探討	4
2.1 社群網路預測	4
2.2 模式探勘	5
第三章 前置作業	7
第四章 系統架構	10
4.1 頻繁序列模式探勘	10
4.2 模型訓練（Model Training）	12
第五章 實作與實驗結果	16
5.1 實驗環境設定	16
5.2 實驗資料庫設定	16
5.3 模型比對	18
第六章 結論	20
參考文獻	22
附錄 英文論文	27
 
圖目錄
圖 1.動態社群網路以及Evolution Database	8
圖 2.系統架構圖	10
圖 3.用戶數量	17
圖 4. 連結數量	17
圖 5.執行時間	17
圖 6. 記憶體用量	17
圖 7.修剪後序列數目	18
圖 8.原始序列數目	18
 
表目錄
表 1. 四個資料庫的基本統計	16
表 2. Positive Hit	18
表 3. Negative Hit	19
表 4.Overall Error	19

[1] L. A. Adamic and E. Adar, "Friends and neighbors on the web," SocialNetworks, vol. 25, no. 3, 2003, pp. 211–230.
[2] S. Asur, S.Parthasarathy, D.Ucar, "An event-based framework for characterizing the evolutionary behavior of interaction graphs," 13th ACM SIGKDD, 2007, pp. 913-921.
[3] J. F. Allen, "Maintaining knowledge about temporal intervals," Proceeding of the Communications of ACM 26(11), 1983, pp. 832–843.
[4] M. Berlingerio, F. Bonchi, B. Bringmann, A. Gionis, "Mining Graph Evolution Rules," Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD’09), 2009, pp. 115-130.
[5] K. M. Borgwardt, H. Kriegel and P. Wackersreuther, "Pattern Mining in Frequent Dynamic Subgraphs," Sixth International Conference on Data Mining (ICDM'06), 2006, pp. 18-822.
[6] Y. Chen, J. Jiang, W. Peng, S. Lee, "An Efficient Algorithm for Mining Time Interval-based Patterns in Large Databases," Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10), 2010, pp. 49-58.
[7] Y. Chen, W. Peng, S. Lee, "Mining Temporal Patterns in Time Interval-Based Data," IEEE Transactions on Knowledge and Data Engineering 27(12), 2015, pp. 3318-3331.
[8] Y. Chi, X. Song, D. Zhou, K. Hino, B. Tseng, "Evolutionary Spectral Clustering by Incorporating Temporal Smoothness," Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07), 2007, pp. 153-162.
[9] D. Chakrabarti, R. Kumar, A. Tomkins, "Evolutionary clustering," Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06), 2006, pp. 554-560.
[10] P. Desikan, J. Srivastava, "Mining Temporally Evolving Graphs," Proceedings of the 6th International Workshop on Knowledge Discovery from the Web (WEBKDD’04), 2004.
[11] C. Fu, M. Zhao, L. Fan, X. Chen, J. Chen, Z. Wu, Y. Xia, Q. Xuan, "Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network," in IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 8, 2018, pp. 1507-1518. 
[12] M. A. Hasan, V. Chaoji, S. Salem, and M. Zaki, "Link prediction usingsupervised learning," Proceeding of the SDM06: workshop on link analysis, counter-terrorism and security, vol. 30, 2006, pp. 798–805.
[13] A. Inokuchi, T. Washio, "A fast method to mine frequent subsequences from graph sequence data," Proceeding of the 8th IEEE International Conference on Data Mining (ICDM’08), 2008, pp. 303-312.
[14] G. Y. Jheng, Y. C. Chen, H. M. Liang, "Evolution pattern mining on dynamic social network," J Supercomput 77, 2021, pp. 6979–6991.
[15] M. Kim, J. Han, "A Particle-and-Density based Evolutionary Clustering Method for Dynamic Networks," Proceedings of the 35th International Conference on Very Large Data Bases (VLDB’09), 2009, pp. 633-633.
[16] M. Lahiri and T. Y. Berger-Wolf, "Structure Prediction in Temporal Networks using Frequent Subgraphs," 2007 IEEE Symposium on Computational Intelligence and Data Mining, 2007, pp. 35-42.
[17] Y. Lin, Y. Chi, S. Zhu, H. Sundaram, B. Tseng,"Facetnet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks," Proceedings of the 17th International Conference on the World Wide Web (WWW’08), 2008, pp. 685-694.
[18] Z. Liu, J. X. Yu, Y. Ke, X. Lin and L. Chen, "Spotting Significant Changing Subgraphs in Evolving Graphs," Proceeding of the 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 917-922.
[19] M. Marjan, N. Zaki and E. A. Mohamed, "Link Prediction in Dynamic Social Networks: A Literature Review," 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), 2018, pp. 200-207.
[20] T. Murata and S. Moriyasu, "Link Prediction of Social Networks Based on Weighted Proximity Measures," IEEE/WIC/ACM International Conference on Web Intelligence (WI'07), 2007, pp. 85-8.
[21] J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, D. Dayal, M. Hsu, "Mining sequential patterns by pattern-growth: the PrefixSpan approach," IEEE Transactions on Knowledge and Data Engineering 16(11), pp. 1424-1440.
[22] A. Popescul, LH. Ungar. "Statistical relational learning for link prediction," in IJCAI, volume 2003, 2003.
[23] G. Palla, A. Barabási,T. Vicsek, "Quantifying social group evolution," Nature 446, 2007, pp.664-667.
[24] G. Qin, J. Yang, L. Gao and J. Li, "Evolution pattern discovery in dynamic networks," Proceeding of the 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2011, pp. 1-6.
[25] H. Wang, W. Hu, Z. Qiu and B. Du, "Nodes' Evolution Diversity and Link Prediction in Social Networks," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 10, 2017, pp. 2263-2274.
[26] B. Wackersreuther, P. Wackersreuther, A. Oswald, C. Böhm and K. M. Borgwardt, "Frequent Subgraph Discovery in Dynamic Networks,"   MLG, 2010, pp. 155-162..
[27] C. You, L. Holder, D. Cook, "Learning Patterns in the Dynamics of Biological Networks," Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), 2009, pp.977-986.
[28] K. Zhu and M. Cao, "A Semantic Subgraphs Based Link Prediction Method for Heterogeneous Social Networks with Graph Attention Networks," 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-8.
[29] M. Zaouzi, D. Rhouma, L. Romdhane, "Community detection in large-scale social networks: state-of-the-art and future directions," Social Network Analysis and Mining 9, Article 23, 2019.
[30] Mobile01. http://www.mobile01.com