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

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指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2021-7-20

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