Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction
學年 114
學期 1
出版（發表）日期 2025-12-02
作品名稱 Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction
作品名稱（其他語言）
著者 Lin Hui; Yi-Cheng Chen
單位
出版者
著錄名稱、卷期、頁數 International Journal of Web and Grid Services 21(3-4), p. 209-307
摘要 In recent years, social networks have grown in popularity, with most people actively engaging on these platforms. These networks hold valuable insights into users' values and interests, allowing us to analyse relationships between connected individuals and even predict potential friendships. However, social networks are dynamic, and their structure evolves over time. To account for this, we employed a dual approach using a bi-phase LSTM autoencoder and a bi-phase LSTM predictor. These tools capture the changing characteristics of social networks and predict future graph structures. We rigorously tested our model on three datasets and compared its performance with other models. The bi-phase LSTM consistently delivered strong results across all datasets. Additionally, the model's hyperparameters were fine-tuned to improve predictive accuracy, demonstrating its reliability in forecasting the evolution of social network structures.
關鍵字 feature extraction; autoencoder; decoder; long short-term memory; dynamic social network
語言 en
ISSN 1741-1114
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度 是
國別 AUS
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128676 )