隨著網路技術的成熟,社群網路已經逐漸融入了人們的生活,人們可以藉由發表訊息在社群網路上,將自己的所見所聞分享給他們的朋友。因此將廣告放在社群網路上,藉由分享訊息的動作,為產品打廣告,如何在社群網路中挑選那些影響力大的人變成了現今重要的議題。這類型的問題稱之為影響力最大化問題,目標在於尋找一個元素數量為 k 的種子集,使得此種子集的影響力為最大。影響力是透過傳播模型計算得知,由於此問題是一個 NP-hard 問題。很難在一個合理的時間內尋找到最佳解。有學者設計一個 greedy 演算法,來尋求此問題的近似解,但效果不是很理想。因此本論文設計了一個高效能的演算法,以新式貪婪演算法降低重複搜尋的核心概念來增強基因演算法的搜尋效率。本論文使用了數個社群網路的資料集,並與其他演算法比較。實驗結果顯示,本論文提出之演算法在種子集較大時,找到的種子集之影響力優於傳統的基因演算法 10%,而且搜尋速度也比一些演化式演算法來的快速。

With the advance of computer and internet technologies, social networks have become an integral part of most people’s life. People can now use social networks to send out messages. They can also share pictures or articles with their friends. For this reason, many businessmen advertise their products by the social networks. As such, one of the critical research topics that come up is how to find people that have the largest influence; i.e., the so-called influence maximization problem. The goal of this problem is to find a k-size seed set which has a maxi-
mum influence with respect to a particular propagation model. For the influence maximization problem is NP-hard, it is obvious that an exhausted search algorithm is not able to fine the solution in a reasonable time. In order to solve this problem, some researchers rely on greedy algorithms to find a approximate solution for this problem, but the quality of the solution is simply not good enough. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the new greedy algorithm and the genetic algorithm (GA), is presented in this thesis. Experimental results show that the proposed algorithm outperforms simple GA by about 10% in terms of the quality, with the speed that is faster than many meta-heuristic algorithms.

論文審定書 i
誌謝 iii
摘要 iv
Abstract v
List of Figures viii
List of Tables ix
Chapter 1 簡介 1
1.1 動機 2
1.2 論文貢獻 3
1.3 論文架構 3
Chapter 2 相關文獻探討 5
2.1 影響力最大化問題 5
2.1.1 定義 5
2.1.2 傳播模型(Propagation Model) 6
2.1.2.1 Independent Cascade Model 7
2.1.2.2 Linear Threshold Model 9
2.1.3 計算影響力 10
2.2 貪婪演算法 10
2.3 Cost-Effective Lazy Forward演算法 12
2.4 新式貪婪演算法 12
2.5 模擬退火演算法 13
2.6 基因演算法 15
2.7 結論 17
Chapter 3 新貪婪基因演算法 18
3.1 演算法設計概念 18
3.2 演算法流程 19
3.2.1 初始化設定 19
3.2.2 更新 21
3.2.3 選擇 21
3.2.4 交配 23
3.2.5 突變 24
3.3 範例 25
Chapter 4 實驗結果 27
4.1 執行環境、參數設定 27
4.2 模擬結果 28
4.2.1 資料集介紹 28
4.2.2 實驗數據分析 29
4.2.2.1 影響力 29
4.2.2.2 標準差 31
4.2.2.3 時間 33
4.3 總結 35
Chapter 5 結論與未來展望 37
5.1 結論 37
5.2 未來展望 37
Bibliography 39

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