淺談社會網絡分析
Introduction to Social Network Analysis
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新手向 BY 3.0 TW
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文月 (Meng-Ying Tsai)
- 聽說有八盤之類的奇妙綽號
- 台大兔子系四年級
- 偶而會在台大開源社出沒
- ❤: 喝淺焙咖啡、唱日卡、嚐甜食
一年365天歡迎餵食,請多指教 ヽ(●´∀`●)ノ
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Vertex
Edge
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Vertex Set V(G)
Edge Set E(G)
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Graph G = (V, E)
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V = {1, 2, 3, 4}
E = {{1, 2}, {1, 3}, {1, 4},
{2, 3}, {3, 4}}
1
2
3
4
{1,2}
{1,3}
{2,3}
{1,4}
{3,4}
1
2
3
4
{1,2}
{1,3}
{2,3}
{1,4}
{3,4}
1 | 2 | 3 | 4 | |
---|---|---|---|---|
1 | 0 | 1 | 1 | 1 |
2 | 1 | 0 | 1 | 0 |
3 | 1 | 1 | 0 | 1 |
4 | 1 | 0 | 1 | 0 |
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Basic Construct
Cohesion
Centrality
Cluster
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* 接下來皆以無向圖作舉例!
Basic Construct
Cohesion
- Density
- Distance
- Connectivity
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Basic Construct
Density
誰會贏? 誰 density 高?
Density
Distance
Connectivity
Basic Construct
Density
D =
|E|
|V| * (|V|-1)
2
1
# of edge
# of total possible edge
=
100%
50%
Density
Distance
Connectivity
Basic Construct
Distance
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Six Degrees of Separation
3.57 Degrees of Separation
Density
Distance
Connectivity
Basic Construct
Distance
geodesic distance d(x,y): shortest path from x to y
x
y
Density
Distance
Connectivity
Basic Construct
Distance
diameter: largest geodesic distance between any pair of nodes
x
y
Density
Distance
Connectivity
Basic Construct
Connectivity
不能沒有你QQ
Density
Distance
Connectivity
Basic Construct
Connectivity
Density
Distance
Connectivity
point connectivity: the min # of nodes that must be removed to disconnect 2 nodes
x
y
1
2
Basic Construct
Connectivity
Density
Distance
Connectivity
太過依賴單一個頂點,
網絡就顯得特別脆弱QQ
Basic Construct
- Degree
- Closeness
- Betweenness
- Eigenvector
Centrality
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常見有 4 種
Basic Construct
Centrality
Degree Centrality: 該 node 的連線越多,centrality 越高
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覺得邊QAQ"
Basic Construct
Centrality
Closeness Centrality: geodesic distance 加總取倒數,distance越短centrality越高。
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覺得邊
QAQ"
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Basic Construct
Centrality
Betweenness Centrality: 越常座落在別人的 geodesic path,centrality 越高。
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Basic Construct
Centrality
Eigenvector Centrality: 基本概念為 degree centrality,跟越重要的 node 連,算分越高。
認識世界知名人物
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認識默默無名小生物
I'm here
>
Pigeon
Degree
Closeness
Betweenness
Eigenvector
Degree
Closeness
Betweenness
Eigenvector
Degree
Closeness
Betweenness
Eigenvector
Degree
Closeness
Betweenness
Eigenvector
Basic Construct
- Clique
- K-core
Cluster
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Basic Construct
Cluster
n-Clique: a maximal subgraph in which every pair of vertices is connected by a path of length n or less
n-Clique
K-core
n=1
n=2
- maximal subgraph:再加入任一頂點就無法維持其性質
- 點跟點之間的距離 ≤ n
Basic Construct
Cluster
n-Clique: a maximal subgraph in which every pair of vertices is connected by a path of length n or less
- 條件嚴格,形成 clique 難
- 每個 clique 都被視為一樣重要
n-Clique
K-core
n=1
n=2
Basic Construct
Cluster
K-core: a subgroup is defined as k-core when member have directed ties to at least K other vertices.
n-Clique
K-core
K = 3
K = 2
K = 1
n-Clique
K-core
K = 3
K = 2
K = 1
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其實標題什麼的,都是胡扯
暗黑破壞神
暗黑復仇者
Dark Web
暗黑網絡
Dark/Covert network!
Cases
Dark Network
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Krebs V.(2001).Mapping Networks of Terrorist Cells. Connections, 24(3)
911 hijacking data
trusted prior contacts
- 鬆散
- 連同組的都要 2 steps way 才能接觸
- 避免其中有人被揪出來,整個網絡被連根拔起
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Cases
Dark Network
Krebs V.(2001).Mapping Networks of Terrorist Cells. Connections, 24(3)
911 hijakcing data
trusted prior contacts + ties
- 太鬆散無法做事QQ
- 讓溝通的順利的meeting!
- 沒有需要時,立即進入冬眠狀態
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Cases
Dark Network
Hughes C.E., Chalmers J., Bright D.A., McFadden M. (2017) Social network analysis of Australian poly-drug trafficking networks: How do drug traffickers manage multiple illicit drugs? Social Networks, 51C
Poly-drug network
藥物買賣(X) 毒品交易(O)
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顏色標示不同毒品種類
大麻 MDMA 甲基安非他命
顏色表示身份
manager resource provider
worker wholesale supplier
Cases
Dark Network
Hughes C.E., Chalmers J., Bright D.A., McFadden M. (2017) Social network analysis of Australian poly-drug trafficking networks: How do drug traffickers manage multiple illicit drugs? Social Networks, 51C
Poly-drug network
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顏色表示身份
manager resource provider
worker wholesale supplier
- 什麼毒品都賣
,都可以賣 - 沒有鬆散、去中心的特質
Cases
Dark Network
Hughes C.E., Chalmers J., Bright D.A., McFadden M. (2017) Social network analysis of Australian poly-drug trafficking networks: How do drug traffickers manage multiple illicit drugs? Social Networks, 51C
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K1
K37
K10~20
betweenness,
degree centrality 高
SNA 還能拿來做什麼?
Cases
Interesting cases
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Managing Creativity in Small Worlds
https://pdfs.semanticscholar.org/9176/2c87d3e73db324322bc03f1e7acb1892786a.pdf
Inventors in Silicon Valley’s Largest Collaborative Cluster
了解網絡中如何產生創新
Cases
Interesting cases
中國歷代人物傳記資料庫
https://projects.iq.harvard.edu/chinesecbdb
Population Density of Biographical Persons
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men who obtained the jinshi degree (Putian, 1050-1100)
China Biographical Database Project (CBDB)
數位人文研究
Cases
Interesting cases
Network of Thrones https://networkofthrones.wordpress.com/
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浪費才能(?)
Thanks for listening (ゝ∀・)⌒☆
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猜猜誰會贏?
令人臉紅心跳的 SNA
一些有點好吃的 SNACK
淺談社會網絡分析
By Meng-Ying Tsai
淺談社會網絡分析
社會網絡分析是一個日漸發展蓬勃的學科,被廣泛的應用在各個領域。包含組織犯罪、疾病傳染、學術傳播都有相關的研究實例。當我們不再聚焦個人的特質去解析組織,轉而去分析一個組織裡的關係時,我們可以從哪些角度去剖析它呢?我們該如何描述一個社會網絡? 本議程將會介紹社會網絡分析的一些基本概念,接著帶大家看看幾個有趣的實例哦!
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