A Bi-Phase LSTM Architecture on Dynamic Social Network Prediction

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蘇恆毅(Heng-Yi Su) 查詢紙本館藏

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資訊管理學系

論文名稱

(A Bi-Phase LSTM Architecture on Dynamic Social Network Prediction)

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至系統瀏覽論文 (2026-7-19以後開放)

摘要(中)

近年來社群網路非常流行，大部分的人都有參與社群網路，社群網路中藏有
非常豐富的資訊，像是價值觀、興趣等。我們可以透過社群網路的互動關係來了
解哪些使用者比較親近，進而推薦使用者未來可能的好友。然而由於時間的演變，
以前可能是好友的友誼關係可能改變了，我們認為社交網路也是會演變的。因此
我們利用 BP-LSTM auto-encoder 和 BP-LSTM predictor，將動態網路圖的特徵保
留起來並用來預測下一個時間點的社交網路圖。我們在三個資料集中評估模型，
並且與不同的模型比較預測結果，實驗結果顯示 BP-LSTM 在三個資料集中都有
相當優秀的表現。最後我們對我們的模型進行參數調整，以達到最佳的預測結果。

摘要(英)

Social networks have become very popular in recent years. Most people participate
in social networks. The social network contains a wealth of information, such as values
and interests. We can learn about those users who are close to each other through the
interaction of social networks, and then recommend users who may be friends in the
future. However, due to the evolution of time, the friendship that may have been friends
may have changed, and we believe that social networks will also evolve. Therefore, we
use BP-LSTM auto-encoder and BP-LSTM predictor to retain the features of the
dynamic network graphs and use it to predict the social network graph at the next point
in time. We evaluated the model in three datasets and compared the prediction results
with different models. The experimental results showed that BP-LSTM performed quite
well in the three data sets. Finally, we adjust the parameters of our model to achieve
the best prediction results.

關鍵字(中)

★ 特徵擷取
★ 長短期記憶網路
★ 動態社交網路

關鍵字(英)

★ Feature Extraction
★ Long Short-Term Memory
★ Dynamic Social Network

論文目次

1. Introduction ..................................................................................................... 1
2. Related Work................................................................................................... 5
2.1 Social network prediction.......................................................................... 5
2.2 Social network prediction on Long Short-Term Memory........................ 8
3. Methodology .................................................................................................... 9
3.1 BP-LSTM auto-encoder model................................................................ 10
3.2 BP-LSTM predictor model...................................................................... 14
4. Performance Evaluation................................................................................ 15
4.1 Datasets .................................................................................................... 16
4.2 Baselines................................................................................................... 17
4.3 Evaluate Metrics...................................................................................... 18
Area Under the ROC Curve .................................................................. 18
Error rate ............................................................................................... 20
4.4 Evaluate baseline models with AUC ....................................................... 21
Contact dataset result ............................................................................ 21
Radoslaw dataset result ......................................................................... 23
Enron dataset result............................................................................... 24
4.5 Evaluate model with error rate ............................................................... 26
Contact dataset result ............................................................................ 27
Radoslaw dataset result ......................................................................... 27
Enron dataset result............................................................................... 27
4.6 Discuss the window size effects on BP-LSTM......................................... 28
4.7 Parameter Setting .................................................................................... 29
Compare LSTM units............................................................................ 29
Compare training epochs....................................................................... 31
Compare the learning rate..................................................................... 33
5. Conclusion...................................................................................................... 35
Reference ............................................................................................................... 36

參考文獻

[1] L. Adamic, E. Adar, “Friends and neighbors on the Web,” Social Networks, Volume
25, Issue 3, Pages 211-230, 2003.
[2] M. Balakrishnan, G. Tv, “A neural network framework for predicting dynamic
variations in heterogeneous social networks”. PLoS ONE 15(4): e0231842, 2020.
[3] A. Bayrak and F. Polat, “Reducing Features to Improve Link Prediction
Performance in Location Based Social Networks, Non-Monotonically Selected
Subset from Feature Clusters,” 2019 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM), 2019, pp. 809-815.
[4] J. Chen, J. Zhang, X. Xu, C. Fu, D. Zhang, Q. Zhang, Q. Xuan, “E-LSTM-D: A
Deep Learning Framework for Dynamic Network Link Prediction,” in IEEE
Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3699-
3712, June 2021.
[5] J. Chen, X. Xu, Y. Wu and H. Zheng, “GC-LSTM: Graph Convolution Embedded
LSTM for Dynamic Link Prediction”. ArXiv, abs/1812.04206, 2018.
[6] L. Dong, Y. Li, H. Yin, H. Le, M. Rui, “The Algorithm of Link Prediction on Social
Network”, Mathematical Problems in Engineering, vol, 2013.
37
[7] 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, pp. 1507-1518, 1 Aug. 2018.
[8] 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. Association for Computing Machinery, 2016.
[9] Z. Hao, “Link Prediction in Online Social Networks Based on the Unsupervised
Marginalized Denoising Model,” in IEEE Access, vol. 7, pp. 54133-54143, 2019.
[10] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory”, Neural
Comput.9,8(November15,1997).
[11] M. Keikha, M. Rahgozar and M. Asadpour, “DeepLink: A novel link prediction
framework based on deep learning”, Journal of Information Science, 2019.
[12] Y. Liang, L. Huang and Z. Wang, 2017, “Link prediction in social network based
on local information and attributes of nodes”, Journal of Physics: Conference Series,
Vol. 887, The 2nd Annual International Conference on Information System and
Artificial Intelligence (ISAI)
[13] M. Lim, A. Abdullah, N. Jhanjhi, and M. Supramaniam, “Hidden Link Prediction
in Criminal Networks Using the Deep Reinforcement Learning”, Technique.
Computers, 2019.
[14] F. Liu, B. Liu, C. Sun, M. Liu, & X. Wang, “Deep Belief Network-Based
Approaches for Link Prediction in Signed Social Networks”, MDPI AG, pp. 2140–
2169, 2015.
[15] M. Marjan, N. Zaki and E. Mohamed, “Link Prediction in Dynamic Social
Networks: A Literature Review,” 2018 IEEE 5th International Congress on
Information Science and Technology (CiSt), pp. 200-207, 2018.
[16] G. Nguyen, J. Lee, R. Rossi, N. Ahmed, E. Koh, and S. Kim,“Continuous-Time
Dynamic Network Embeddings”. In Companion Proceedings of the The Web
Conference 2018 (WWW ′18). International World Wide Web Conferences Steering
Committee, Republic and Canton of Geneva, CHE, 969–976, 2018.
[17] A. Papadimitriou, P. Symeonidis, Y. Manolopoulos, “Fast and accurate link
prediction in social networking systems,” Journal of Systems and Software, Vol. 85,
No.9, pp. 2119-2132, 2012.
[18] J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, J. Tang, “DeepInf: Social Influence
Prediction with Deep Learning,” In Proceedings of the 24th ACM SIGKDD
38
International Conference on Knowledge Discovery & Data Mining (KDD ′18),2018.
[19] S. Shalforoushan and M. Jalali, “Link prediction in social networks using
Bayesian networks,” The International Symposium on Artificial Intelligence and
Signal Processing (AISP), pp. 246-250, 2015.
[20] H. Shao, L. Wang and Y. Ji, “Link Prediction Algorithms for Social Networks
Based on Machine Learning and HARP,” in IEEE Access, vol. 7, pp. 122722-
122729, 2019.
[21] U. Sharma and B. Minocha, “Link Prediction in Social Networks: A Similarity
score based Neural Network Approach”, Proceedings of the Second International
Conference on Information and Communication Technology for Competitive
Strategies (ICTCS), 2016.
[22] N. Srivastava, E. Mansimov, and R. Salakhutdinov, “Unsupervised learning of
video representations using LSTMs”. In Proceedings of the 32nd International
Conference on International Conference on Machine Learning - Volume 37
(ICML′15). JMLR.org, 843–852, 2015.
[23] 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, pp. 2263-2274, 1 Oct. 2017.
[24] A. Zareie, R. Sakellariou, “Similarity-based link prediction in social networks
using latent relationships between the users”, Sci Rep. 10, 20137, 2020.
[25] R. Zeng, Y. Ding and X. Xia, “Link prediction based on dynamic weighted social
attribute network,” 2016 International Conference on Machine Learning and
Cybernetics (ICMLC), pp. 183-188, 2016.
[26] J. Zhang and C. Zhang, “A Study on Link Prediction Algorithm Based on Users’
Privacy Information in the Weighted Social Network,” 2019 3rd International
Conference on Data Science and Business Analytics (ICDSBA), pp. 7-12, 2019.
[27] L. Zhao et al., "T-GCN: A Temporal Graph Convolutional Network for Traffic
Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 21,
no. 9, pp. 3848-3858, Sept. 2020.
[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), pp. 1-8, 2020.
[29] K. Zhu, M. Cao and H. Lu, “MALP: A More Effective Meta-Paths Based Link
Prediction Method in Partially Aligned Heterogeneous Social Networks,” 2019
IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI),
39
pp. 644-651, 2019.
[30] Digital report: https://wearesocial.com/digital-2020
[31] Contact Datasets: collected by University of Namur Research:
http://konect.cc/networks/contact/
[32] Radoslaw Datasets: collected by R. Michalski Research:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6Z3C
GX
[33] Enron Datasets: collected by Federal Energy Regulatory Commission Research:
https://www.cs.cmu.edu/~enron/

指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2021-7-20

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