淺談社會網絡分析

Introduction to Social Network Analysis

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文月 (Meng-Ying Tsai)

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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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