정보사회와소셜네트워크 6주차 - 소셜네트워크의이해1

정보사회와 소셜 네트워크

소셜 네트워크의 이해 1

김영진

국 국민대학교 경영대학원 0


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Social Capital

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정보사회와 소셜 네트워크 4


Network Effects

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Creativity


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(연결) (결속)

접속

자원동원

혁신

영향력

이익창출

다른 집단/사람을 중개해주는 것 “우리”를 만드는 것

친근감

소속감

신뢰

지원

공동체 형성


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Bridging Social capital


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Bonding Social capital


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A real example: Padget and Ansell:

“Robust Action and the Rise of the Medici”

“Political Groups” in the attribute sense do not

seem to exist, so P&A turn to the pattern of network relations among families.

This is the BLOCK

reduction of the full 92 family network.


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16 http://article.joins.com/news/article/article.asp?total_id=1687037


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network network


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N ENGL J MED 357;4 July 26, 2007


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어떤 경로로 비만이 잘 전염되는가?

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Metcalf’s Law


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조직 구조

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Complicated !


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어떻게 복잡한 관계를 쉽게 이해할 수 있을까?

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10-minute Break


아래 노드 중에 한 명과 친구가 되야한다면?

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자신이 하나의 위치에 놓여야 한다면…


Find Important / Influence Actor

• A method that distinguishes “important” actors, those who are at the “center” of the network ̶ in concept, straightforward ̶ in practice, complicated

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중앙성(Centrality)


Approaches

• Degree

• Closeness

• Betweenness

• Power Centrality


Degree centrality

多多益善

얼마나 많은 연결을 갖고 있는가? 얼마나 많은 팔로워(친구)를 가지고 있는가?


Closeness centrality

多多益善

얼마나 중앙에 있는가 있는가? 얼마나 소문을 퍼뜨리기 좋은 위치인가?


Betweenness centrality (사이 중앙성, 매개중앙성)

얼마나 소문이 퍼지는 경로 사이에 많이 놓여있는가?


Betweenness Centrality:


Power Centrality

狐假虎威


R Script Editor

Console Etc.

Environment & History

단축 아이콘 메뉴바


1. 무료 / 범용성 Free (available on any platform)

2. 프로그래밍 기반 Fully programmable

3. 반복가능 Reproducible -Every step is recorded

4. 놀라운 시각화 기능 Stunning visuals

5. 확장성 Expandability

6. 배우기 쉽다 Easy to learn - Good online

documentation; and the community

Why Use R?


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• install.packages("igraph") • install.packages("igraphdata")

• library(igraph) • library(igraphdata) • data(UKfaculty) • plot(UKfaculty)

##http://goo.gl/uBik3V • V(UKfaculty)$size <- 5 • V(UKfaculty)$label <- NA • E(UKfaculty)$arrow.size <- 0.3 • E(UKfaculty)$color <- "#33333333" • plot(UKfaculty)

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• V(UKfaculty)$dc <- degree(UKfaculty)

• k_dc_top_three <- head(sort(degree(UKfaculty), decreasing=TRUE), n=3)

• V(UKfaculty)$color <- ifelse(V(UKfaculty)$dc %in% k_dc_top_three, "red", "white")

• plot(UKfaculty)

• V(UKfaculty)$cc <- closeness(UKfaculty)

• k_cc_top_three <- head(sort(closeness(UKfaculty), decreasing=TRUE), n=3)

• V(UKfaculty)$color <- ifelse(V(UKfaculty)$cc %in% k_cc_top_three, "red", "white")

• plot(UKfaculty)

• V(UKfaculty)$bc <- betweenness(UKfaculty)

• k_bc_top_three <- head(sort(betweenness(UKfaculty), decreasing=TRUE), n=3)

• V(UKfaculty)$color <- ifelse(V(UKfaculty)$bc %in% k_bc_top_three, "red", "white")

• plot(UKfaculty)

• V(UKfaculty)$ec <- evcent(UKfaculty)$vector

• k_ec_top_three <- head(sort(evcent(UKfaculty)$vector, decreasing=TRUE), n=3)

• V(UKfaculty)$color <- ifelse(round(V(UKfaculty)$ec,7) %in% round(k_ec_top_three,7), "red", "white")

• plot(UKfaculty)

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N:4039 E: 88234


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The Top 10 nodes by Degree Centrality


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The Top 10 nodes by Betweenness Centrality


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The Top 10 nodes by Closeness Centrality


Social Influence in Twitter

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Indegree : follower의 수 Mention: Twitter에서 직접 언급되는 것 Retweet: 내 글을 남들이 공유시키는 것

정보사회와소셜네트워크 6주차 - 소셜네트워크의이해1

Presentations & Public Speaking