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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
dc.contributor.author | Pei-Lun Liao | en |
dc.contributor.author | 廖沛倫 | zh_TW |
dc.date.accessioned | 2021-06-15T13:51:07Z | - |
dc.date.available | 2020-12-01 | |
dc.date.copyright | 2015-12-01 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-10-06 | |
dc.identifier.citation | [1] M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion
and inference. In KDD, 2010. [2] M. Gomez-Rodriguez, D. Balduzzi, and B. Schölkopf. Uncovering the temporal dynamics of diffusion networks. In ICML, 2011. [3] Manuel Gomez-Rodriguez, Jure Leskovec, and Bernhard Schölkopf. Modeling information propagation with survival theory. In ICML, 2013. [4] M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. Structure and dynamics of information pathways in online media. In WSDM, 2013. [5] S. Myers and J. Leskovec. On the convexity of latent social network inference. In NIPS, 2010. [6] Kazumi Saito, Ryohei Nakano, and Masahiro Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES, 2008. [7] Senzhang Wang, Xia Hu, Philip S. Yu, and Zhoujun Li. Mmrate: Inferring multiaspect diffusion networks with multi-pattern cascades. In KDD, 2014. [8] Ming-Hao Yang, Chung-Kuang Chou, and Ming-Syan Chen. Cluster cascades: Infer multiple underlying networks using diffusion data. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, 2014. [9] Simon Bourigault, Cedric Lagnier, Sylvain Lamprier, Ludovic Denoyer, and Patrick Gallinari. Learning social network embeddings for predicting information diffusion. In WSDM, 2014. [10] Liaoruo Wang, Stefano Ermon, and John E. Hopcroft. Feature-enhanced probabilistic models for diffusion network inference. In PKDD, 2012. [11] Nan Du, Le Song, Hyenkyun Woo, and Hongyuan Zha. Uncover topic-sensitive information diffusion networks. In AISTATS, 2013. [12] J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD, 2009. [13] Jayanta K. Ghosh. Survival and event history analysis: A process point of view by odd o. aalen, Ørnulf borgan, håkon k. gjessing. International Statistical Review, 77:463–464, 2009. [14] A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39(1): 1–38, 1977. [15] H. Robbins and S. Monro. A stochastic approximation method. The Annals of Mathematical Statistics, pages 400–407, 1951. [16] Jure Leskovec and Rok Sosič. SNAP: A general purpose network analysis and graph mining library in C++. http://snap.stanford.edu/snap, 2014. [17] Jesse Davis and Mark Goadrich. The relationship between precision-recall and roc curves. In ICML, 2006. [18] J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani. Kronecker graphs: An approach to modeling networks. The Journal of Machine Learning Research, 11:985–1042, 2010. [19] Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, 2014. [20] Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. Gephi: An open source software for exploring and manipulating networks. In ICWSM, 2009. [21] Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: bringing order to the web. 1999. [22] King wa Fu, Chung hong Chan, and M. Chau. Assessing censorship on microblogs in china: Discriminatory keyword analysis and the real-name registration policy. Internet Computing, IEEE, 17:42–50, 2013. [23] Seth A. Myers, Chenguang Zhu, and Jure Leskovec. Information diffusion and external influence in networks. In KDD, 2012. [24] Qingbo Hu, Sihong Xie, Shuyang Lin, Wei Fan, and Philip S. Yu. Frameworks to encode user preferences for inferring topic-sensitive information networks. In SDM, 2015. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51812 | - |
dc.description.abstract | 在日常生活中我們能輕易地觀察到謠言傳遞的現象,然而直接觀察
謠言傳遞的過程是困難的。目前推測資訊傳遞過程的問題得到越來越 多的關注,因為當我們了解資訊傳遞的過程,我們便可以增減資訊傳 遞的影響力。例如流行病學家可以在疾病傳播的過程中減少感染的人 數,或是貨品銷售業者能夠提升他們廣告影響的效益。最近有很多的 方法被提出來解決這樣的問題,研究人員考慮資訊傳播於一個傳遞網 路上,在網路中連結或朋友關係表示資訊傳播的路徑;而節點或使用 者是資訊的接受者。其他人考慮資訊傳播於多個網路上,每一個網路 對應一個特定的主題,例如戰爭、科技或體育。在這個研究中,我們 觀察到實際資訊傳播的模式,發現大部分的節點傾向於直接分享或接 受訊息來源的資訊。換句話說,當資訊來自於二手的來源之後,影響 力便會開始衰減。這是因為人們喜歡直接關注消息來源,並不想成為 最後一個得知消息的人。因此,我們延伸之前的研究,提出一個機率 模型,這個模型模擬了我們觀察的資訊傳播模式,並推論隱藏的傳遞 網路和節點間傳遞速率。我們也提出一個有效率的最佳化方法去找出 有最大可能性的模型參數。實驗的結果顯示我們的模型能夠擊敗其他 的方法在多個不同的傳播網路結構和傳播過程中。 | zh_TW |
dc.description.abstract | In our daily life rumors are spread among many people but diffusion processes and spreading paths behind rumors are usually hidden. The problem of finding this hidden process is getting more and more attention since after understanding the process, we can manipulate the diffusion speed of the process. For example, epidemiologists and the government can block a spread of a disease. Companies and goods sellers can facilitate adoptions of a product. To model the hidden information diffusion, it is usually assumed that information spreads in an underlying diffusion network where nodes, e.g. users, are receivers which adopt an information piece and edges, e.g. friendships, are transmission paths. In this work, we observe the pattern of information propagation that most of nodes are inclined to share the first-hand information. In other words, the virality of an information piece will generally decay as it becomes rephrased. We propose a generative model with the first-hand sharing patten (FASTEN), in order to infer the hidden networks and transmission rates between nodes. We further propose an efficient optimization method to infer the parameters of FASTEN. Experimental results show that FASTEN outperforms several state-of-the-arts algorithms on both synthetic and real datasets for network inference. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:51:07Z (GMT). No. of bitstreams: 1 ntu-104-R02921027-1.pdf: 1192379 bytes, checksum: 5e7fe583c1c4e20ffc1c2ff8a2f46407 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員會審定書 i
Acknowledgments ii 中文摘要 iii Abstract iv Contents v List of Figures vii List of Tables viii 1 Introduction 1 2 Related Work 4 3 Preliminary 6 3.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Additive risk model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4 FASTEN Model 11 4.1 Generative Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5 Experimental Results 16 5.1 Experimental setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.1.1 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.1.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2.1 Data generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3 Real data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.3.1 Memetracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.3.2 Weiboscope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6 Conclusion 30 Bibliography 31 | |
dc.language.iso | en | |
dc.title | 基於直接分享訊息模式推論多個傳遞網路 | zh_TW |
dc.title | FASTEN:Uncovering Multiple Diffusion Networks Using the First-Hand
Sharing Pattern | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳孟彰(Meng-Chang Chen),鄭卜壬(Pu-Jen Cheng),歐建志(Jian-Chih Ou) | |
dc.subject.keyword | 傳播,傳播網路,傳播分析,社群網路,生存分析, | zh_TW |
dc.subject.keyword | Diffusion,Diffusion Network,Cascade Analytic,Social Network,Survival Analysis, | en |
dc.relation.page | 33 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-10-06 | |
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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