請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49100
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 | Su-Chen Lin | en |
dc.contributor.author | 林素貞 | zh_TW |
dc.date.accessioned | 2021-06-15T11:15:59Z | - |
dc.date.available | 2018-08-26 | |
dc.date.copyright | 2016-08-26 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-20 | |
dc.identifier.citation | [1] A. Beutel, B. A. Prakash, R. Rosenfeld, and C. Faloutsos. Interacting viruses in networks: Can both survive? In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012.
[2] S. Bharathi, D. Kempe, and M. Salek. Competitive influence maximization in social networks. In Workshop on Internet and Network Economics, 2007. [3] A. Borodin, Y. Filmus, and J. Oren. Threshold models for competitive influence in social networks. In Workshop on Internet & Network Economics, 2010. [4] C. Budak, D. Agrawal, and A. E. Abbadi. Limiting the spread of misinformation in social networks. In International World Wide Web Conference, 2011. [5] T. Carnes, C. Nagarajan, S. M. Wild, and A. van Zuylen. Maximizing influence in a competitive social network: A follower’s perspective. In International Conference on Electronic Commerce, pages 351–360, 2007. [6] H.-H. Chen, Y.-B. Ciou, and S.-D. Lin. Information propagation game: a tool to acquire human playing data for multiplayer influence maximization on social networks. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2012. [7] W. Chen, A. Collins, R. Cummings, T. Ke, Z. Liu, D. Rincon, X. S. snd Yajun Wang, W. Wei, and Y. Yuan. Influence maximization in social networks when negative opinions may emerge and propagate. In SIAM International Conference on Data Mining, 2011. [8] W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2010. [9] W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2009. [10] P. Domingos and M. Richardson. Mining the network value of customers. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2001. [11] A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. A data-based approach to social influence maximization. International Conference on Very Large Data Bases, 2011. [12] A. Goyal, W. Lu, and L. V. S. Lakshmanan. Simpath: An efficient algorithm for influence maximization under the linear threshold model. In IEEE International Conference on Data Mining, 2011. [13] X. He, G. Song, W. Chen, and Q. Jiang. Influence blocking maximization in social networks under the competitive linear threshold model. In SIAM International Conference on Data Mining, 2012. [14] J. Hu and M. P.Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003. [15] J. Hu and M. P. Wellman. Multiagent reinforcement learning: Theoretical framework and an algorithm. In International Conference on Machine Learning, 1998. [16] K. Jung, W. Heo, and W. Chen. Irie: Scalable and robust influence maximization in social networks. In IEEE International Conference on Data Mining, 2012. [17] D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2003. [18] J. Kostka, Y. A. Oswald, and R. Wattenhofer. Word of mouth: Rumor dissemination in social networks. In International Colloquium on Structural Information and Communication Complexity, 2008. [19] T.-T. Kuo, S.-C. Hung, W.-S. Lin, S.-D. Lin, T.-C. Peng, and C.-C. Shih. Assessing the quality of diffusion models using real-world social network data. In International Conference on Technologies and Applications of Artificial Intelligence, Nov 2011. [20] J. Leskovec. Stanford large network dataset collection http://snap.stanford.edu/data/. 2014-10-28. [21] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2007. [22] H. Li, S. S. Bhowmick, J. Cui, Y. Gao, and J. Ma. Getreal: Towards realistic selection of influence maximization strategies in competitive networks. In ACM SIGMOD International Conference on Management of Data, 2015. [23] Y. Lin and J. C. Lui. Analyzing competitive influence maximization problems with partial information: An approximation algorithmic framework. volume 91, pages 187–204, 2015. [24] M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In International Conference on Machine Learning, 1994. [25] W. Lu, F. Bonchi, A. Goyal, and L. V. Lakshmanan. The bang for the buck: Fair competitive viral marketing from the host perspective. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013. [26] W. Lu, W. Chen, and L. V. Lakshmanan. From competition to complementarity: Comparative influence diffusion and maximization. In International Conference on Very Large Data Bases, 2015. [27] H.-C. Ou, C.-K. Chou, and M.-S. Chen. Influence maximization for complementary goods: Why parties fail to cooperate? In ACM International Conference on Information and Knowledge Management, 2016. [28] M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2002. [29] R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998. [30] C.-H. Tsai, J.-K. Lou, W.-C. Lu, and S.-D. Lin. Exploiting rank-learning models to predict the diffusion of preferences on social networks. In International Conference on Advances in Social Network Analysis and Mining, 2014. [31] J. Tsai, T. H. Nguyen, and M. Tambe. Security games for controlling contagion. In Association for the Advancement of Artificial Intelligence, 2012. [32] J. von Neumann and O. Morgenstern. Theory of games and economic behavior. In Princeton University Press, 1944. [33] C. Watkins. Learning from Delayed Rewards. PhD thesis, Cambridge University, 1989. [34] C. Watkins and P. Dayanr. Q-learning. Machine Learning, 8:279–292, 1992. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49100 | - |
dc.description.abstract | 隨著社群媒體的興起,人們藉由社群網路接受到的訊息越來越多,受到的影響也越來越大。因此有很多公司想借由這種社群上的影響力來做行銷。這些公司會挑選某些關鍵人物在社群網路上發表產品訊息,藉由人們在社群上互相傳遞訊息的模式,期望此訊息能影響到最多的人,得到最多的客戶。這類挑選關鍵人物經由社群傳遞訊息,使預期影響範圍達到最大的問題我們稱為影響力最大化問題。然而不同的公司若有類似產品或服務,他們的市場就會重疊,需要去競爭有限的客戶資源。考慮到這些公司的競爭關係,這篇論文採用學習為基礎的框架去解決這種社群網路下多回合競爭影響力最大化問題。我們提出了一個資料導向的方法,利用後設學習的概念,在強化學習的架構下去最大化長期影響力的期望值。當公司在挑選關鍵人物去競爭客戶時,我們的方法不只考慮到社群間的資訊還考慮了對手公司的策略。在多回合的問題下,我們的方法可以達到長期影響力總和的最大化,而不是近視短利地去追求每一回的最大化。我們分別在對手策略已知、對手策略未知但可持續對他訓練,和對手策略未知且不可持續對他訓練的這三個情況下,提出了各自的解法。最後在實驗結果中顯示出在我們提出的架構下,我們的方法能達到預期的效果,並驗證了事前提出的假設。 | zh_TW |
dc.description.abstract | Considering nowadays companies providing similar products or services compete with each other for resources and customers, this work proposes a learning-based framework to tackle the multi-round competitive influence maximization problem on a social network. We propose a data-driven model leveraging the concept of meta-learning to maximize the expected influence in the long run. Our model considers not only the network information but also the opponent's strategy while making a decision. It maximizes the total influence in the end of the process instead of myopically pursuing short term gain. We propose solutions for scenarios when the opponent's strategy is known or unknown and available or unavailable for training. We also show how an effective framework can be trained without manually labeled data, and conduct several experiments to verify the effectiveness of the whole process. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:15:59Z (GMT). No. of bitstreams: 1 ntu-105-F95921025-1.pdf: 5284578 bytes, checksum: ad7cfacbaa9992cdff04e6b97ac508c6 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌謝v
摘要vii Abstract ix 1 Introduction 1 1.1 Competitive Influence Maximization . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Problem Statement 9 2.1 Diffusion Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Multi-Round Competitive Influence Maximization . . . . . . . . . . . . 10 3 Related Work 13 3.1 Competitive Influence Maximization with Opponent Strategy Known . . 13 3.2 Competitive Influence Maximization with Opponent Strategy Unknown . 14 4 Strategy-Oriented Reinforcement-Learning 17 4.1 Preliminary: Reinforcement Learning . . . . . . . . . . . . . . . . . . . 17 4.2 Design of Strategy-Oriented Reinforcement-Learning . . . . . . . . . . . 18 5 Strategy-Oriented Reinforcement-Learning with Opponent Strategy Known 25 5.1 STORM-Q . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6 Strategy-Oriented Reinforcement-Learning with Opponent Strategy Unknown 29 6.1 Opponent Strategy Unknown but Available for Training . . . . . . . . . . 30 6.2 Opponent Strategy Unknown and Unavailable for Training . . . . . . . . 30 6.2.1 STORM-QQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.2.2 STORM-MM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6.3 Complexity Analysis of STORM . . . . . . . . . . . . . . . . . . . . . . 34 7 Experiments 37 7.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 7.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 7.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 8 Conclusion 47 Bibliography 49 | |
dc.language.iso | en | |
dc.title | 以機器學習為基礎之社群網路下的競爭影響力最大化 | zh_TW |
dc.title | A Learning-based Framework to Handle Multi-round Competitive Influence Maximization on Social Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),陳良弼(Arbee L.P. Chen),曾新穆(Vincent S. Tseng),莊坤達(Kun-Ta Chuang),沈之涯(Chih-Ya Shen) | |
dc.subject.keyword | 競爭影響力最大化,強化學習,社群網路,賽局理論,資料導向,後設學習, | zh_TW |
dc.subject.keyword | Competitive Influence Maximization,Reinforcement Learning,Social Network,Game Theory,Data-driven,Meta-learning,Multi-agents, | en |
dc.relation.page | 52 | |
dc.identifier.doi | 10.6342/NTU201602345 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-21 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf 目前未授權公開取用 | 5.16 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。