social network analysis : methods and applications ch 1,2
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Social Network Analysis : Methods and Application Chapter 1, 2 summaryTRANSCRIPT
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Part I. Networks, Rela-tions, and
Structures(chap 1, 2)박건우
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1. Social Network Analysis in the Social and Behavioral Sciences
2. Social Network Data
목차
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1. Social Network Analysis in the Social and Behavioral Sciences
2. Social Network Data
목차
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Important concepts◦ Relations : fundamental concept of network theories◦ Actors and their actions are viewed as interdependent rather than inde-
pendent, autonomous units◦ Relational ties between actors are channels for transfer or “flow” of re-
sources◦ Network models focusing on individuals view the network structural en-
vironment as providing opportunities for or constraints on individual ac-tion
◦ Network models conceptualize structure as lasting patterns of relations among actors
분석의 대상이 개인이 아닌 개인들의 collection 과 그것의 연결들 그런 연결의 규칙성과 패턴이 structure 를 낳음 ! 분석의 대상이 개인이 아닌 관계이기 때문에 , 그에 따른 measurement
방법도 전통적인 사회학과 다르다 -> 이 책에서는 Network Measurement 에 대해 다룸 !
1.1 Social Network Perspective
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Social Network 는 본질적으로 interdisciplinary◦ social theory and application◦ formal mathematical, statistical, and computing methodology
1. Empirical Motivation◦ 실제적인 어떤 현상들을 이해하기 위해 방법론이 발전해 옴 .
2. Theoretical Motivation◦ 이론적인 개념들을 또한 네트워크 메소드 발전을 촉진 .◦ social group, structural balance, social role, etc.
3. Mathematical Motivation◦ SNA 에 수학적 개념을 이용하기 시작◦ graph theory, statistical and probability theory, algebraic mdodels.
Social network analysis provides a precise way to define important social concepts, a theoretical alternative to the assumption of in-dependent social actors, and a framework for testing theories about structured social relationships
1.2 Historical and Theoretical Founda-tions
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Social Network Analysis : Social entities 간의 linkage 와 implication을 이해하기
Actors : “Social entities” Relational Tie : actor 간 사이에 정의되는 “ linkage” Dyad : a pair of actors and the tie between them Triad : a subset of three actors and the ties among them
◦ balance theory Subgroup : Dyad, Triad 는 actor 가 2 개 , 3 개 . 그것을 일반화 한 것 Group : the collection of all actors on which ties are to be mea-
sured Relation : collection of ties Social Network : a finite set or sets of actors and the relation or re-
lations
1.3 Fundamental Concepts in Network Analysis
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Most basic feature(and distinctive from other perspectives)◦ the use of structural or relational information
측정의 기본 단위는 tie 이지만 , actor 의 attribute 도 이용 가능하다 .◦ 통계적 분석 가능
regression, t-test
1.4 Distinctive Features of Network Theory and Mea-surement
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1. Social Network Analysis in the Social and Behavioral Sciences
2. Social Network Data
목차
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Social Network Data : at least one structural variable on a set of actors
무얼 측정할 것인가 ?? 어떤 방법을 써야 하는가 ??
2.1.1 Structural and Composition Variables 2.1.2 Modes 2.1.3 Affiliation Variables
2.1 Introduction: What Are Network Data
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Structural variables : pairs of actors 에 대해 측정되는 값 . a.k.a. tie Composition variables : actor attributes 의 measurement
◦ individual level 로 정의되는 값 .
2.1.1 Structural and Composition Vari-ables
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structural variable 이 측정될 수 있는 distinct set of entities 즉 , 노드 종류의 개수
ex 1> one-mode network : 노드의 타입이 다 같은 네트워크 ex 2> two-mode network : 노드의 종류가 두 가지 . sender 와 receiver
network
2.1.2 Modes
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Affiliation Network : special type of two-mode network Affiliation Network 에서의 첫번째 set 은 actor set,
두 번째 set 은 set of events
Membership 류를 나타낼 때 사용될 수 있다
2.1.3 Affiliation Variables
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Social Network Data : at least one structural variable on a set of actors
무얼 측정할 것인가 ?? 어떤 방법을 써야 하는가 ??
2.2.1 What is your population? 2.2.2 Sampling
2.2 Boundary Specification and Sam-pling
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actor 의 set boundary 정하기 어느 경우에는 쉽지만 , 어느 경우에는 어렵다 .
1) realist approach : actor 가 perceive 하는 만큼 2) nominalist approach : researcher 가 의도한 만큼
2.2.1 What is your population?
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네트워크에만 적용되는 sampling 기법들이 있음◦ snowball sampling◦ chain method : small world technique
2.2.2 Sampling
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Mode : structural variable 이 측정되는 number of sets of entities 즉 , mode 의 수는 네트워크에서 구별되는 entities 의 종류를 의미함 .
2.3.1 One-mode Networks 2.3.2 Two-mode Networks 2.3.3 Ego-centered and Special Dyadic Networks
2.3 Types of Networks
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“single” set of actors
Actors : people, subgroup, organization, collectives 등등 . 뭐든 될 수 있다 .
Relations : single set of actor 에 정의되는 다양한 종류의 relation◦ 개인 관계 , 거래 , 이동 , 역할 , 친족 관계 등등
Actor attributes
2.3.1 One-mode Networks
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“two” sets of actors, or a set of actors and a set of events
1) Two Sets of Actors◦ Actors : 2 set 은 서로 다른 type◦ Relations : with a set, between different actors
2) One Set of Actors and One Set of Events◦ Affiliation network, membership network◦ Actors : set of actors◦ Events : set of events◦ Attribute : actor attributes
2.3.2 Two-Mode Networks
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1. Ego-centered network◦ ego-perceived network◦ “Social Support” 와의 관계
2. Special Dyadic Network◦ some pair 에게만 relation 가능하도록
2.3.3 Ego-centered and Special Dyadic Networks
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Network Data 의 측정과 수집을 어떻게 할 것인가 ? 발생할 수 있는 오류와 , 정확도를 나타내는 개념
2.4.1 Measurement 2.4.2 Collection 2.4.3 Longitudinal Data Collection 2.4.4 Measurement Validity, Reliability, Accuracy, Error
2.4 Network Data, Measurement and Collection
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측정을 어느 단위로 할 것이고 (Unit of Observation), 어떤 단위로 Model-ing 할 것이며 (Modeling Unit), 관계에 대한 수치화 ( 수량화 ) 는 어떻게 할 것인가 ?(Relational Quantification)
Unit of observation◦ 보통 actor 단위로 측정 .◦ 우리 연구에서는 모든 데이터를 커버하므로 의미 없음
Modeling Unit◦ 네트워크가 model 되고 summarize 되는 단위 ?◦ Actor, Dyad, Triad, Subgroup, Set of actors or network
Relational Quantification◦ 관계를 수적으로 어떻게 표현 ?◦ dichotomous : 0 or 1. 있거나 없거나 .◦ valued : 강도 표현
2.4.1 Measurement
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어떤 식으로 수집 ?◦ 이것도 우리 연구에서는 모아진 데이터를 이용하므로 접근법이 다르다 .
Questionnaire◦ Roster vs Free recall◦ Free vs Fixed choice◦ Rating vs Complete Ranking
Interview Observation Archival Records Other
◦ cognitive social structure◦ experimental◦ ego-centered◦ small world◦ diary
2.4.2 Collection
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시간 변화에 따른 데이터 수집 two research question
◦ 1) process 가 시간에 따라 어떻게 변화했는가◦ 2) 과거로 미래 예측할 수 있는가 .
dynamic analysis 우리의 경우에는 time snapshot 의 링크 정보를 수집해야 한다
2.4.3 Longitudinal Data Collection
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Accuracy◦ true structure 를 얼마나 잘 measure 했는 가 .◦ 우리는 데이터를 보니까 accuracy 는 높음
Validity◦ 측정하고자 한 것을 정말로 측정했는가◦ measure 의 단위가 제대로 되었는가
Reliability◦ 반복된 measurement 가 같은 값을 주는가 ?◦ 어떤 경우에는 재 측정 자체가 불가능 할 수 있다 (ex> longitudinal)
Measurement Error◦ true value 와 observed value 의 차이◦ sampling 문제로 인한 측정값이 변할 수 있는 문제
2.4.4 Measurement Validity, Reliability, Accu-racy, Error
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Pass
2.5 Data Sets Found in These Pages