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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Jui-Hsiu Hsu | en |
| dc.contributor.author | 許睿修 | zh_TW |
| dc.date.accessioned | 2021-07-10T22:00:56Z | - |
| dc.date.available | 2021-07-10T22:00:56Z | - |
| dc.date.copyright | 2021-03-05 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-01-20 | |
| dc.identifier.citation | [1] L. Belli, S. I. Ktena, A. Tejani, A. Lung-¬Yut¬-Fon, F. Portman, X. Zhu, Y. Xie, A. Gupta, M. Bronstein, A. Delić, et al. Privacy¬-preserving recommender systems challenge on twitter’s home timeline. arXiv preprint arXiv:2004.13715, 2020. [2] N. Du, L. Song, H. Woo, and H. Zha. Uncover topic-sensitive information diffusion networks. In Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, pages 229–237, 2013. [3] M. Gomez-Rodriguez, D. Balduzzi, and B. Schölkopf. Uncovering the temporal dynamics of diffusion networks. In Proceedings of the 28th International Conference on Machine Learning, pages 561–568, 2011. [4] M. Gomez Rodriguez, J. Leskovec, and B. Schölkopf. Structure and dynamics of information pathways in online media. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining, pages 23–32, 2013. [5] A. Guille, H. Hacid, C. Favre, and D. A. Zighed. Information diffusion in online social networks: A survey. ACM Sigmod Record, 42(2):17–28, 2013. [6] X. He and Y. Liu. Not enough data? joint inferring multiple diffusion networks via network generation priors. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining, pages 465–474, 2017. [7] 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. [8] P. Liao, C. Chou, and M. Chen. Uncovering multiple diffusion networks using the firsthand sharing pattern. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 63–71, 2016. [9] H. Robbins and S. Monro. A stochastic approximation method. The Annals of Mathematical Statistics, 22(3):400–407, 1951. [10] D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th International Conference on World Wide Web, pages 695–704, 2011. [11] C. Su, X. Guan, Y. Du, X. Huang, and M. Zhang. Toward capturing heterogeneity for inferring diffusion networks: A mixed diffusion pattern model. Knowledge-Based Systems, 147:81–93, 2018. [12] S. Wang, X. Hu, P. S. Yu, and Z. Li. Mmrate: Inferring multi-aspect diffusion networks with multi-pattern cascades. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 1246–1255, 2014. [13] M. Yang, C. Chou, and M. Chen. Cluster cascades: Infer multiple underlying networks using diffusion data. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2014. [14] M. Zhang, Y. Du, G. Zhang, Y. Xie, and F. Cao. Online social information propagation analysis based on time-delay mixture diffusion model. In 2019 IEEE 5th International Conference on Multimedia Big Data, pages 331–337. IEEE, 2019. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77418 | - |
| dc.description.abstract | 在社會網絡的領域內，有關網絡推論的問題，主要在於研究使用者間傳播資訊的影響力強弱。由於資訊擴散的軌跡是隱藏的，且不同主題間的資訊各有不同的傳播模式，所以需要透過大量的傳播資料來推論擴散網絡。而在過去的一些文獻內，已有提出許多方法來推論多個特定主題的擴散網絡。然而，這些方法僅考慮了資訊的擴散特性或使用者的屬性，卻未顧及使用者的資訊散播行為本身，是否會對資訊擴散造成影響。本篇論文，我們提出一個生成混合模型，名叫「UBRate」。它充分考慮了不同使用者間的行為差異和影響力來推論多個擴散網絡，且我們分別利用模擬資料和真實資料來驗證UBRate 的效益。最終，實驗結果成功表明UBRate 能有效地推論多個擴散網絡，並且優於其他基準模型。 | zh_TW |
| dc.description.abstract | The network inference problem (NIP) has been studied to infer hidden information diffusion networks. Since information diffusion patterns differ across topics, several approaches have been proposed to infer multiple topic-specific diffusion networks. However, these approaches consider only the diffusion properties of information or user attributes and ignore differences in user behavior. In this work, we propose UBRate, a generative mixture model which leverages the distribution and impact of distinct types of user behavior to infer multiple diffusion networks. Experimental results on both synthetic data and real-world data show that the proposed model effectively infers the diffusion networks and outperforms other baseline models. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T22:00:56Z (GMT). No. of bitstreams: 1 U0001-0901202116523200.pdf: 1821833 bytes, checksum: d8186c6007265b114771b04759f2bd30 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書i 誌謝ii 摘要iv Abstract v Contents vi List of Figures viii List of Tables ix 1 Introduction 1 2 Related Work 4 3 Preliminary 6 3.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.2 User behavior . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.3 Topic distribution . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.4 Multiple diffusion networks . . . . . . . . . . . . . . . . . . 8 3.1.5 User behavior distribution . . . . . . . . . . . . . . . . . . . 8 3.1.6 Impact of user behavior . . . . . . . . . . . . . . . . . . . . 8 3.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Methodology 10 4.1 Survival analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.2 UBRate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5 Experiment 15 5.1 Data description and prepocessing . . . . . . . . . . . . . . . . . . . 15 5.1.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.1.2 Real-world data . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.2 Baseline models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.3 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.4.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.4.2 Real-world data . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.5.1 Convergence analysis . . . . . . . . . . . . . . . . . . . . . . 20 5.5.2 Results on synthetic data . . . . . . . . . . . . . . . . . . . . 20 5.5.3 Results on real-world data . . . . . . . . . . . . . . . . . . . 23 5.5.4 Case study: impact of user behavior . . . . . . . . . . . . . . 23 6 Conclusion 25 References 26 | |
| dc.language.iso | en | |
| dc.subject | 使用者行為 | zh_TW |
| dc.subject | 社會網絡 | zh_TW |
| dc.subject | 網絡推論 | zh_TW |
| dc.subject | 擴散網絡 | zh_TW |
| dc.subject | 存活分析 | zh_TW |
| dc.subject | social network | en |
| dc.subject | user behavior | en |
| dc.subject | survival analysis | en |
| dc.subject | diffusion network | en |
| dc.subject | network inference | en |
| dc.title | 利用使用者行為推論多個擴散網絡 | zh_TW |
| dc.title | Inferring Multiple Diffusion Networks with User Behavior | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 王釧茹(Chuan-Ju Wang) | |
| dc.contributor.oralexamcommittee | 楊得年(De-Nian Yang),葉彌妍(Mi-Yen Yeh),帥宏翰(Hong-Han Shuai) | |
| dc.subject.keyword | 社會網絡,網絡推論,擴散網絡,存活分析,使用者行為, | zh_TW |
| dc.subject.keyword | social network,network inference,diffusion network,survival analysis,user behavior, | en |
| dc.relation.page | 28 | |
| dc.identifier.doi | 10.6342/NTU202100037 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-01-21 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資料科學學位學程 | zh_TW |
| 顯示於系所單位： | 資料科學學位學程 | |
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