期刊論文
| 學年 | 102 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2014-04-01 |
| 作品名稱 | CIM: Community-Based Influence Maximization in Social Networks |
| 作品名稱（其他語言） | |
| 著者 | Chen, Yi-Cheng; Peng, Wen-Chih; Lee, Wan-Chien; Lee, Suh-Yin |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | New York: Association for Computing Machinery, Inc. |
| 著錄名稱、卷期、頁數 | ACM Transactions on Intelligent Systems and Technology 5(2), Article 25, pp.1-31 |
| 摘要 | Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness. |
| 關鍵字 | Community detection;diffusion models;influence maximization;social network analysis |
| 語言 | en_US |
| ISSN | 2157-6904 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98125 ) |