教學大綱表
請遵守智慧財產權,勿使用非法影印教科書,避免觸法。
課程名稱 (中文) 社會網路分析
(英文)
開課單位 資訊經營研究所
課程代碼 N5790B
授課教師 曾筱珽
學分數 3.0 必/選修 選修 開課年級 研究所
先修科目或先備能力:具備資訊管理/程式設計/行銷管理相關基礎能力
課程概述與目標:本課程主要目的為讓修課學生在社群網路力量不容小覷的現代環境中,讓自身具備以資訊管理思維聆聽網路社群聲音的能力,並將分析結果進一步結合行銷管理知識,將社群網路分析結果應用於行銷策略的規劃與制訂。透過本課程,您將了解:
1. 社群網路分析的理論與技術
2. 社群網路資料的如何蒐集、分析、視覺化與解釋
3. 如何應用整理與分析出來的脈絡與結果應用於行銷範疇
教科書 Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Thousand Oaks, CA: Sage.
參考教材 1. Barabási A-L. (2015). Network Science. Retrieved from http://networksciencebook.com/
2. 謝邦昌、鄭宇庭、謝邦彥 (2017)。玩轉社群:文字大數據實作。硬是愛數據應用股份有限公司。
3. 吳燦銘 (2017)。網路行銷的12堂必修課:SEO、社群、廣告、直播、Big Data。博碩。
課程大綱 學生學習目標 單元學習活動 學習成效評量 備註
單元主題 內容綱要
1 Introduction 1.Why networks?
2.What are networks?
3.Types of relations
4.Goals of analysis
5.Network variables as explanatory variables
6.Network variables as outcome variables
Understand the background and theoretical basis of social network analysis  
2 Social network analysis & Precision marketing Social network analysis & Precision marketing Understand the background and theoretical basis of social network analysis  
3 Mathematical foundations 1.Graphs
2.Paths and components
3.Adjacency matrices
4.Ways and modes
5.Matrix products
Understand the background and theoretical basis of social network analysis  
4 Research design 1.Experiments and field studies
2.Whole-network and personal-network research designs
3.Sources of network data
4.Types of nodes and types of ties
5.Actor attributes
6.Sampling and bounding
7.Sources of data reliability and validity issues
8.Ethical considerations
Understand social network marketing analytics and Research  
5 Data collection 1.Network questions
2.Question formats
3.Interviewee burden
4.Data collection and reliability
5.Archival data collection
6.Data from electronic sources
Understand social network marketing analytics and Research  
6 Data management 1.Data import
2.Cleaning network data
3.Data transformation
4.Normalization
5.Cognitive social structure data
6.Matching attributes and networks
7.Converting attributes to matrices
8.Data export
Understand social network marketing analytics and Research  
7 Multivariate techniques used in network analysis 1.Multidimensional scaling
2.Correspondence analysis
3.Hierarchical clustering
Understand social network marketing analytics and Research  
8 Visualization 1.Layout
2.Embedding node attributes
3.Node filtering
4.Ego networks
5.Embedding tie characteristics
6.Visualizing network change
7.Exporting visualizations
8.Closing comments
Understand techniques of visualization  
9 Midterm Midterm Midterm
  • 期中考
  •  
    10 Testing Hypotheses 1.Permutation tests
    2.Dyadic hypotheses
    3.Mixed dyadic–monadic hypotheses
    4.Node level hypotheses
    5.Whole-network hypotheses
    6.Exponential random graph models
    7.Stochastic actor-oriented models (SAOMs)
    Interpreting of social network marketing  
    11 Characterizing Whole Networks 1.Cohesion
    2.Reciprocity
    3.Transitivity and the clustering coefficient
    4.Triad census
    5.Centralization and core–periphery indices
    Interpreting of social network marketing  
    12 Centrality 1.Basic concept
    2.Undirected, non-valued networks
    3.Directed, non-valued networks
    4.Valued networks
    5.Negative tie networks
    Interpreting of social network marketing  
    13 Subgroups 1.Cliques
    2.Girvan–Newman algorithm
    3.Factions and modularity optimization
    4.Directed and valued data
    5.Computational considerations
    6.Performing a cohesive subgraph analysis
    Interpreting of social network marketing  
    14 Equivalence 1.Structural equivalence
    2.Profile similarity
    3.Block models
    4.The direct method
    5.Regular equivalence
    6.The REGE algorithm
    7.Core–periphery models
    Interpreting of social network marketing  
    15 Analyzing Two-mode Data 1.Converting to one-mode data
    2.Converting valued two-mode matrices to one-mode
    3.Bipartite networks
    4.Cohesive subgroups and community detection
    5.Core–periphery models
    6.Equivalence
    Interpreting of social network marketing  
    16 Large Networks 1.Reducing the size of the problem
    2.Choosing appropriate methods
    3.Sampling
    4.Small-world and scale-free networks
    Interpreting of social network marketing  
    17 Ego Networks 1.Personal-network data collection
    2.Analyzing ego network data
    3.Example 1 of an ego network study
    4.Example 2 of an ego network study
    Interpreting of social network marketing  
    18 Project Reports Project Reports Project Reports
  • 專題
  •  

    教學要點概述:
    教材編選: ■ 自編教材 ■ 教科書作者提供
    評量方法: 專題:40%   期中考:40%   其他評量:20%  
    教學資源: □ 教材電子檔 □ 課程網站
    扣考規定:http://eboard.ttu.edu.tw/ttuwebpost/showcontent-news.php?id=504

    研究所
    核心能力 專題 期中考 其他評量
    核心能力一 學術研究能力 4/10 4 4 4
    核心能力二 技術發展能力 5/10 5 5 5
    核心能力三 服務管理能力 1/10 1 1 1