淺談社會網絡分析
Introduction to Social Network Analysis
新手向 BY 3.0 TW
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
Vertex
Edge
Vertex Set V(G)
Edge Set E(G)
Graph G = (V, E)
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 |
Basic Construct
Cohesion
Centrality
Cluster
* 接下來皆以無向圖作舉例!
Basic Construct
Cohesion
- Density
- Distance
- Connectivity
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
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
常見有 4 種
Basic Construct
Centrality
Degree Centrality: 該 node 的連線越多,centrality 越高
覺得邊QAQ"
Basic Construct
Centrality
Closeness Centrality: geodesic distance 加總取倒數,distance越短centrality越高。
覺得邊
QAQ"
Basic Construct
Centrality
Betweenness Centrality: 越常座落在別人的 geodesic path,centrality 越高。
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
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
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
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
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
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
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)
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
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
K1
K37
K10~20
betweenness,
degree centrality 高
SNA 還能拿來做什麼?
Cases
Interesting cases
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
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/
浪費才能(?)
Thanks for listening (ゝ∀・)⌒☆
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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