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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳銘憲 | |
dc.contributor.author | Chung-Kuang Chou | en |
dc.contributor.author | 周崇光 | zh_TW |
dc.date.accessioned | 2021-07-11T14:39:14Z | - |
dc.date.available | 2022-10-03 | |
dc.date.copyright | 2017-10-03 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-05-25 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77996 | - |
dc.description.abstract | 資訊傳播為社群網路中的自然現象。網路中每個節點對於一則訊息的採納行為會受到多種因素所影響,這些因素包含訊息的新鮮程度與熱門程度等。過去研究提出的擴散模型大多僅考量單一或多個固定數量的影響因素,但影響節點採納決策的因素會隨著不同的應用情境而有所不同,或者新的情境存在從未被考量過的影響因素。若將過去的擴散模型直接應用在有新的影響因素的情境下,過去的模型將難以準確反應出資訊的擴散狀況或將無法適用於新的情境。再者,社群網路中兩個節點的訊息接收存在著不確定性,例如線上社群網路中的使用者多半僅具有有限的時間能閱讀朋友所產生的新文章。
本論文提出了新的擴散模型,此模型能考量影響採納行為的多重因素和訊息接收的不確定性,因素的數量可因不同應用情境而不同,本論文也提出了與考量影響採納行為的因素獨立的參數學習架構,因此在面對不同的採納影響因素下,本論文提出的擴散模型將能在不修改學習架構的情況下套用於新的應用情境。此擴散模型在合成資料與真實資料的感染預測與擴散估計皆相當有效。 | zh_TW |
dc.description.abstract | Information diffusion is a natural phenomenon occurring in social networks. The adoption behavior of a node toward an information piece in a social network can be affected by different factors, e.g. freshness and hotness. Previously, many diffusion models are proposed to consider one or several fixed factors. In fact, the factors affecting adoption decision of a node are different from one to another and may not be seen before. For a different scenario of diffusion with new factors, previous diffusion models may not model the diffusion well, or are not applicable at all. Moreover, uncertainty of information exposure intrinsically exists between two connected nodes. For instance, a user in online social networks usually has limited attention, and thus the user may not read every information from her neighbors. Such uncertainty causes modeling diffusion more challenge in social networks.
In the dissertation, our aim is to design a diffusion model in which factors considered are flexible to be extended and changed and the uncertainly of information exposure is explicitly tackled. We further propose a framework of learning parameters of the model, which is independent of factors considered. Therefore, with different factors, our diffusion model can be adapted to more scenarios of diffusion without requiring the modification of the learning framework. We conduct comprehensive experiments on synthetic and real datasets and show that our diffusion model is effective on two important tasks of information diffusion, namely activation prediction and spread estimation. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:39:14Z (GMT). No. of bitstreams: 1 ntu-106-D99921023-1.pdf: 1090575 bytes, checksum: 3e7919c128018c4590ce2827620c7f85 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | Acknowledgements v
摘要vii Abstract ix Notation Table xiii 1 Introduction 1 1.1 Motivation and Overview of the Dissertation . . . . . . . . . . . . . . . 1 1.2 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . 4 2 Related Work 5 2.1 Diffusion Models in Social Networks . . . . . . . . . . . . . . . . . . . 5 2.2 Positive and Unlabeled Learning . . . . . . . . . . . . . . . . . . . . . . 8 3 MFAD Model 11 3.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Learning with source information 15 4.1 Learning Classifiers of Nodes . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.1 Obtaining Unlabeled Instances . . . . . . . . . . . . . . . . . . . 16 4.1.2 Training a Node’s Classifier . . . . . . . . . . . . . . . . . . . . 17 4.2 Learning the Transmission Probability . . . . . . . . . . . . . . . . . . . 18 5 Learning without source information 25 5.1 Obtaining Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Parameter Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6 Activation Prediction and Spread Estimation 29 6.1 Activation Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.2 Spread Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 7 Experimental Results 35 7.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 7.2 Results on Synthetic Datasets . . . . . . . . . . . . . . . . . . . . . . . . 41 7.3 Results on Real Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 45 7.4 Different Classification Models for Adoption Decision in MFAD with Source Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 8 Conclusions and Future Work 51 Bibliography 53 | |
dc.language.iso | en | |
dc.title | 於社群網路中考量多重因素之資訊擴散模型 | zh_TW |
dc.title | Multiple Factors-Aware Diffusion in Social Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 林守德,張嘉惠,陳良弼,黃仁暐 | |
dc.subject.keyword | 社群網路,資訊擴散,擴散模型,感染預測,擴散估計, | zh_TW |
dc.subject.keyword | social networks,information diffusion,diffusion models,activation prediction,spread estimation, | en |
dc.relation.page | 57 | |
dc.identifier.doi | 10.6342/NTU201700852 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-05-26 | |
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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