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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 廖婉君(Wanjiun Liao) | |
dc.contributor.author | Tsunghan Wu | en |
dc.contributor.author | 吳宗翰 | zh_TW |
dc.date.accessioned | 2021-06-17T04:38:31Z | - |
dc.date.available | 2023-08-15 | |
dc.date.copyright | 2018-08-15 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-07 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70788 | - |
dc.description.abstract | 基於時序社群網路的網路結構分析是理解人類社交行為和互動的重要研究課題。為了系統性地認識時序網路,我們提出了兩個基本問題,並據此提出了一個通用框架,而該框架可追蹤時序網路中的動態變化,並對該動態變化建模,進一步預測此時序網路之未來網路結構。本文分別對時序人-物網路(二分網路)及時序社群網路(單一關聯網路)進行研究。在時序二分網路上,我們引入了時間二分投影(TBP),將所有使用者的時間訊息整合之後,並據此建立物件轉移機率圖(ITG)。基於物件轉移機率圖,我們提出了一個可對使用者及物件之間連結給分的評分函數(STEP),可用於執行新連結的預測任務。此外,針對時序社群網路,我們引入了時間拉普拉斯特徵映射(TLE)來給定時序網路中各個節點的特徵向量序列,進而利用有限脈衝響應瀘波器學習使用者的特徵向量序列並建立預測模型。我們依此建立一個通用的預測框架,可用於社群偵測、連結預測及節點排名等網路分析應用。最後,我們更引進了遞歸神經網路作為建立時序預測模型的工具以達到更準確的預測效能。為了驗證預測框架的有效性,我們在合成數據集及真實數據上都作了實驗。在時序二分網路上,我們使用了DBLP,Flickr,Delicious作為真實數據集;而在時序社群網路上,我們使用了Infectious,Haggle及RealityMining 作為實驗用的真實數據集。我們的實驗結果顯示,我們所提出的預測框架在追蹤特徵向量序列及預測未來網路結構方面是非常有效的。 | zh_TW |
dc.description.abstract | Structural network analysis for temporal social networks is an essential discipline for comprehending human behaviors and interactions on social networks. For systematically interpreting the temporal networks, we raise two fundamental questions and propose a general framework to track, model, and predict the structures of time-varying networks. In this dissertation, both temporal user-item (bipartite) networks and temporal social (unipartite) networks are scrutinized respectively. We introduce temporal bipartite projection (TBP) to socially aggregate the temporal information among users and represent the item transition tendencies within an item transition graph (ITG). Based on the ITG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair which is for performing the new link prediction task. Furthermore, we introduce temporal Laplacian eigenmaps (TLE) for determining the sequence of latent feature vectors for each node from temporal networks. A general prediction framework is proposed based on the results of TLE, which use the Finite Impulse Response (FIR) filter to learn the dynamics of evolving latent feature vectors of users. Then, the predicted feature vectors are used for various network analysis applications, including community detection, link prediction, and node ranking. Besides, we also use the recurrent neural networks (RNNs) to model the temporal latent feature vectors for better accuracy. To validate the effectiveness of our frameworks, we conduct various experiments based on our synthetic datasets and real-world datasets such as DBLP, Flickr, Delicious for temporal user-item networks and Infectious, Haggle, Reality Mining for temporal social networks. Our experimental results show that our framework is very effective in tracking latent feature vectors and predicting future network structures. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:38:31Z (GMT). No. of bitstreams: 1 ntu-107-D99921033-1.pdf: 6528391 bytes, checksum: 0353eb3b8ad6a52b2e5c9b3e7da5b9da (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 iii 摘要 v Abstract vii Contents ix List of Figures xv List of Tables xvii 1 Introduction 1 1.1 Temporal Bipartite Projection and Link Prediction for Temporal Bipartite Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Tracking Dynamics of Temporal Networks . . . . . . . . . . . . . . . . . 6 1.3 Structural Network Analysis for Temporal Networks . . . . . . . . . . . 7 1.4 Prediction of Temporal Networks with Recurrent Neural Networks . . . . 9 1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Link Prediction for Bipartite Networks 11 2.1 Temporal Bipartite Projection . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 Temporal Projection Graph . . . . . . . . . . . . . . . . . . . . . 12 2.1.2 Weight Assignment for Transitions . . . . . . . . . . . . . . . . 15 2.1.3 An Illustrating Example of the TBP Method and the TPG . . . . . 17 2.2 Bipartite Link Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 The STEP Method . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.2 An Example of the STEP Method . . . . . . . . . . . . . . . . . 22 2.3 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.2 Transition Tendency and PageRank . . . . . . . . . . . . . . . . 23 2.3.3 Link Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3 Network Tracking, Prediction, and Applications 33 3.1 Review of Spectral Graph Theory . . . . . . . . . . . . . . . . . . . . . 33 3.2 Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.1 Modeling the System Dynamics . . . . . . . . . . . . . . . . . . 35 3.2.2 Prediction of the Network Evolution . . . . . . . . . . . . . . . . 38 3.2.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . 39 3.2.4 Network Embedding and Latent Feature Vectors . . . . . . . . . 40 3.2.5 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.2 Tracking and Predicting Latent Feature Vectors . . . . . . . . . . 48 3.3.3 Performance Evaluation for Applications . . . . . . . . . . . . . 53 3.4 summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4 Network Prediction with Recurrent Neural Networks 63 4.1 Review of Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . 63 4.1.1 Review of LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1.2 Review of GRU . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Prediction Framework with RNNs . . . . . . . . . . . . . . . . . . . . . 68 4.3 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3.1 Parameter Settings for Experiments . . . . . . . . . . . . . . . . 69 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3.3 Computation Time . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5 Conclusion and Future Work 95 Bibliography 97 | |
dc.language.iso | en | |
dc.title | 動態社群網路追蹤及其網路結構分析 | zh_TW |
dc.title | Tracking Dynamics of Temporal Social Networks and
Applications in Structural Network Analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 張正尚(Cheng-Shang Chang),陳銘憲(Ming-Syan Chen),曾新穆(Vincent S. Tseng),彭文志(Wen-Chih Peng),謝宏昀(Hung-Yun Hsieh) | |
dc.subject.keyword | 時序二分網路,時序社群網路,佩奇排名,譜圖理論,拉普拉斯特徵映射,有限脈衝響應濾波器,遞歸神經網路,社群偵測,連結預測,節點排名, | zh_TW |
dc.subject.keyword | temporal bipartite networks,temporal social networks,PageRank,spectral graph theory,Laplacian eigenmaps,finite impulse response filter,recurrent neural network,community detection,link prediction,node ranking, | en |
dc.relation.page | 107 | |
dc.identifier.doi | 10.6342/NTU201802691 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-08 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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