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研究生:蘇厚安
研究生(外文):SU, HOU-AN
論文名稱:以雙重群聚係數門檻值提升重疊社群分群品質之研究
論文名稱(外文):Research on Improving Overlapping Community Clustering Quality using Dual Clustering Coefficient Thresholds
指導教授:蘇怡仁蘇怡仁引用關係
口試委員:胡武誌陳昭和蘇怡仁
口試委員(外文):HU, WU-CHIHCHEN, CHAO-HOSU, YI-JEN
口試日期:2023-07-06
學位類別:碩士
校院名稱:樹德科技大學
系所名稱:資訊工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:47
中文關鍵詞:Social NetworkClustering Coefficient
外文關鍵詞:Social NetworkClustering Coefficient
相關次數:
  • 被引用被引用:2
  • 點閱點閱:38
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
隨著社群網路相關技術的進步,使用者透過社群媒體平台互動變的更加頻繁。現今手機之便利性讓使用者間的互動資訊具有後續分析的價值,企業只要能夠有效分析特定族群的互動訊息,加以利用就可以創造新的商機。
本研究透過局部群聚係數Local Clustering Coefficient(LCC)來實現高品質的重疊社群發現結果,先80/20法則來篩選種子節點,經由調整LCC及Average Clustering Coefficient(ACC)相關參數來提升整體的分群品質,以Modularity Q來評估整體的分群品質。最後與局部群聚係數進行比較,雙重群聚係數的EQ值為0.3,高於局部群聚係數的EQ值,顯示本研究在動態調整門檻值方面能夠有效提升整體的分群品質。

With the advancement of social network-related technologies, user interactions through social media platforms have become more frequent. The convenience of mobile devices nowadays allows the interaction information between users to have value for subsequent analysis. If enterprises can effectively analyze the interaction messages of specific demographics and make use of them, new business opportunities can be created.
This study achieves high-quality overlapping community detection through the Local Clustering Coefficient (LCC). It applies the 80/20 rule to select seed nodes and enhances the overall clustering quality by adjusting the relevant parameters of LCC and Average Clustering Coefficient (ACC). The Modularity Q is used to evaluate the overall clustering quality. Finally, compared to the local clustering coefficient, the EQ value of the dual clustering coefficient is 0.3, which is higher than the EQ value of the local clustering coefficient. This indicates that our study is effective in improving the overall clustering quality through dynamic threshold adjustment.

中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
一、緒論 1
1.1 前言 1
1.2 研究動機 3
1.3 論文架構 4
二、相關技術與文獻探討 5
2.1 社群網路分析(Social Network Analysis, SNA) 5
2.2 小世界理論(Small World Theory) 6
2.3 帕雷托法則(Pareto Principle) 7
2.4 重疊式社群(Overlapping Community) 8
2.5 群聚係數(Clustering Coefficient) 9
2.6 Clique Percolation Method(CPM) 12
2.7 Modularity Q 15
2.8 Extension of Modularity Q (EQ) 17
三、研究方法與系統設計 20
3.1 Data pre-processing 20
3.2 Seeding nodes 22
3.3 Expansion phase 25
3.3.1 Expansion phase(階段一) 26
3.3.2 Expansion phase(階段二) 30
3.4 分群流程圖介紹 31
四、系統實作與實驗結果 37
4.1 實驗資料集 37
4.2 實驗結果 38
五、結論 43
參考文獻 45


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