教學大綱 Syllabus

科目名稱:社群網路

Course Name: Introduction to Social Networks

修別:群

Type of Credit: Partially Required

3.0

學分數

Credit(s)

10

預收人數

Number of Students

課程資料Course Details

課程簡介Course Description

In this course, the following topics will be presented and discussed: social media analysis, blogs and friendship network analysis, email and messaging analytics, influence spreading and viral marketing, social reputation and trust, user profiling and recommendation systems, social media searches, expertise and authority discovery, community identification, link prediction, collaborative data analysis, and data mining with social factors.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明


    課程目標與學習成效Course Objectives & Learning Outcomes

    Some websites own considerable amount of data, e.g., the user topology of Facebook contains billions of nodes. For a large variety of social networking applications, community detection is the one of the most basic issues for mining their data. Moreover, new topics emerge for modeling the user behaviors with the abundant social information, e.g., credibility mining, user interest modeling, user demographics and social strategy inference, advertisement targeting, fraud/anomaly detection, influence probability learning. On the other hand, analyzing social links provides fundamental knowledge for different applications, e.g., link prediction for friend/item recommendation, social influence for viral marketing, and anchor link inference for identity authentication. Also, graph pattern mining is one of the most important topics for graph data mining as well as the pairwise shortest path query and triangle counting. Furthermore, to avoid malice adversary obtaining users’ real identities of each corresponding node, privacy-preserving graph mining plays a very important role when social network data is used in practical commercial sales. The clustering and classification of documents in social media are also important for social networks.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type

    1. Data Mining in Social Network

    2. Frequent Pattern Mining
    3. Clustering
    4. Classification

    5. Classification
    6. Statistics Property of Social Network

    7. Community Discovery
    8. Midterm
    9. Node Classification
    10. Link prediction
    11. Privacy in Social Network Text Mining

    12. Text Mining in Social Network

    13.Project Proposal
    14. Social Influence
    15. Social Tagging
    16. Project Presentation

    授課方式Teaching Approach

    70%

    講述 Lecture

    10%

    討論 Discussion

    10%

    小組活動 Group activity

    10%

    數位學習 E-learning

    0%

    其他: Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    check in the class

    指定/參考書目Textbook & References

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權,務必使用正版書籍。 Respect Copyright.

    課程相關連結Course Related Links

    
                

    課程附件Course Attachments

    課程進行中,使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    需經教師同意始得使用 Approval

    列印