隨著網路媒體越來越發達，在虛擬世界中，人與人之間的關係已然形成一種社群網路，而這些關係會隨著時間有所改變，稱為動態社群網路。為了將網路作為圖形處理並考慮其時間特性，我們設置時間點作為固定間隔，並將一系列快照劃分為動態社群網路，以觀察節點的連結趨勢並預測哪些節點將出現或消失在圖中。 此過程稱為「動態網路連結預測」(Dynamic Network Link Prediction ,DNLP)， 這意味著我們可以根據其歷史行為來推斷將與目標用戶建立關係的特定人群。我們提出了一種新的基於生成對抗網路(Generative Adversarial Network , GAN)模型，叫Soc-GAN，結合長短期記憶(Long Short-Term Memory, LSTM)，用於動態網路的連結預測，並能處理長期預測任務，以捕獲序列之間的向量相關性，再加以分類、判斷生成的預測快照是否與真實資料相似。 同時，它也具有更穩健的能力來預測將在下一個網路圖中將出現或消失的連結。

With the development of online media, in the online virtual world, the relationship between people has formed a kind of social network, and these relationships will change over time, called dynamic social network. In order to deal the network as a graph and consider its time characteristics, we set the time point as a fixed interval and divide a series of snapshots into a dynamic social network to observe the connection trend of nodes and predict which nodes will appear or disappear in the graph. This process is called "Dynamic Network Link Prediction" (DNLP), which means that we can infer a specific group of people who will establish a relationship with the target user based on their historical behavior. We propose a new model based on Generative Adversarial Network (GAN), called Soc-GAN, combined with Long Short-Term Memory (LSTM), to do the link prediction of dynamic social networks and deal with the long-term prediction tasks for capture the relevance of vectors between the sequences and classify, distinguish whether the generated prediction snapshot is similar to real data. At the same time, it also has a more robust ability to predict the links that are going to appear or disappear in the next network graphs.

Table of Contents
Chinese Abstract…………………………I 
Abstract…………………………III  
Table of Contents…………………………IV 
List of Figures…………………………V 
List of Tables…………………………VI 
Chapter 1 Introduction…………………………1 
Chapter 2 Related Work…………………………4 
   2.1 Social Network Prediction…………………………4 
   2.2 Generative Adversarial Network…………………………9 
Chapter 3 Methodology…………………………12 
   3.1 Problem Definition…………………………12 
      3.1.1 Dynamic Network…………………………12 
      3.1.2 Dynamic Network Link Prediction…………………………12 
   3.2 Soc-GAN Framework…………………………13 
      3.2.1 Generator (LSTM)…………………………14 
      3.2.2 Discriminator…………………………16 
   3.3 Training Process…………………………16 
Chapter 4 Experiments…………………………18 
   4.1 Dataset…………………………18 
   4.2 Baselines…………………………19 
   4.3 Evolution Metrics…………………………19 
   4.4 Experimental Results…………………………21 
   4.5 Parameter Setting…………………………23 
Chapter 5 Conclusion…………………………26 
Reference…………………………27 

List of Figures 
Figure 1: Dynamic social network prediction…………………………2 
Figure 2: An illustration of network evolution…………………………13 
Figure 3: The architecture of Soc-GAN…………………………14 
Figure 4: The structure of the LSTM cell…………………………15 
Figure 5: TPR vs. FPR at different classification thresholds…………………………21 
Figure 6: AUC (Area under the ROC Curve)…………………………21 
Figure 7: AUC performance of Soc-GAN models on different sliding window 
size…………………………24 
Figure 8: AUC performance of Soc-GAN models on different generator 
layers…………………………24 
Figure 9: AUC performance of Soc-GAN models on different learning rate…………………………25 
Figure 10: AUC performance of Soc-GAN models on different discriminator 
layers…………………………25 
Figure 11: AUC performance of Soc-GAN models on different epochs…………………………25   

List of Table 
Table 1: The basic statistics of the three datasets…………………………18 
Table 2: ROC confusion matrix…………………………20 
Table 3: AUC performance of different models on three datasets…………………………23

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