藉由預期負面傳播解決影響力阻擋最大化問題｜國立臺灣科技大學博碩士論文系統

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研究生：	林奎勝 Kuei-Sheng Lin
論文名稱：	藉由預期負面傳播解決影響力阻擋最大化問題 Solving the Influence Blocking Maximization Problem by the Expected Negative Influence
指導教授：	戴碧如 Bi-Ru Dai
口試委員:	黃俊隆 Jiun-Long Huang 戴碧如 Bi-Ru Dai 沈之涯 Chih-Ya Shen 陳怡伶 Yi-Ling Chen
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	62
中文關鍵詞：	影響力阻擋最大化、社群媒體、多競爭者擴散模型、謠言阻擋
外文關鍵詞：	influence blocking maximization, social network, competitive influence diffusion, rumor blocking
相關次數：	點閱：213 下載：0
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近年來，因為社群媒體的成長，使得資訊得以快速的傳遞。然而，社群媒體的發展也使得負面消息，例如謠言或是錯誤的知識等，被迅速的傳播。找出一個好方法來阻止謠言的擴散變得刻不容緩。影響力阻擋最大化問題(IBM) 目標即為找出正種子來阻擋負面消息傳播，然而現存的方法無法在阻擋點質量以及計算時間中取得有效的權衡。因此，我們提出了BENI 演算法以及新的圖架構ENIG 來有效的解決影響力阻擋最化問題，並於真實資料集中驗證BENI 演算法可以有效的阻擋負面消息傳遞。

With the growing of social networks, information can be propagated fast in social networks. However, social networks also leads bad information such as rumor and misinformations can spread faster simultaneously. Influence Blocking Maximization (IBM) problem is studied to find positive seeds to block the spread of negative information as much as possible. Existing solutions can not effectively trade-off between seed quality and time cost. In this work, we propose BENI algorithm and devise a new graph structure ENIG to solve IBM problem. As verified by experiments on real world datasets,BENI algorithm is able to block negative information with high efficiency and effectiveness.

指導教授推薦書...................................i
論文口試委員審定書................................ii
Abstract ........................................iii
論文摘要.......................................iv
致謝..........................................v
Contents ........................................vi
List of Figures .....................................viii
List of Tables .....................................x
List of Algorithms ...................................xi
1 Introduction ....................................1
2 Related Work ...................................3
2.1 Influence Maximization Problem ......................3
2.2 Competitive Influence Maximization Problem ...............4
2.3 Influence Blocking Maximization Problem .................4
3 Preliminaries ....................................6
3.1 Competitive Diffusion Models .......................6
3.2 Problem Definition .............................7
4 Proposed Algorithm ................................10
4.1 Construct ENIG ..............................11
4.2 Compute Blocking Score ..........................15
4.3 Initialize ENIG ..............................18
4.4 Update ENIG ...............................19
5 Experiment ....................................23
5.1 Experimental Settings ............................23
5.2 Results of experiments ...........................26
5.2.1 The experiments of BENI .....................26
5.2.2 Compare with other algorithms ..................30
6 Conclusions and Future Works ..........................48
6.1 Conclusions .................................48
6.2 Future Works ................................48
References .......................................49
                                

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全文公開日期 2024/08/15 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)

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