Transcript Social Networks - Centre for Policy Modelling

Social Networks:
Agent-based Modelling and Social
Network Analysis with PAJEK
Richard Taylor¹ and Gindo Tampubolon²
¹Centre for Policy Modelling, Manchester Metropolitan University
²Centre for Research on Innovation and Competition, University of Manchester
ESRC Research Methods Festival, Oxford, 17th-20th July 2006,
&
Oxford Spring School, Dept. of Politics and International Relations
Outline of Tutorial
Morning
Afternoon
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Graph theory and Social
Networks
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Social Network Analysis
measures and techniques
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Agent-based Modelling
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PAJEK Software
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2 Demo Models
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ABM Methodology
JinGirNew friendship
Innovation Model
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Software utilities
JAVA
REPAST
REALJ
Concluding session:
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Discussion on issues for
combining the 2 methods
Methodological Overview
• Quantitative and numerical
• Formal language
• Indices / metrics
• Visualisation
• A range of phenomena (e.g…)
SNA Background
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Social interactions are structured; structure (i.e. the
social climate) determines the nature of the parts
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the perceptions, behaviour, and the limitations and
opportunities for action
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Concern for structure of informal relations/groupings
as opposed to formal roles (i.e. micro-foundational)
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Relations which the individuals themselves recognise
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Mechanisms by which social aggregates are sustained
and transformed over time
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To identify leaders, isolated individuals, sub-groups etc.
Findings
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Systematic interdependence between the attitudes held
by different individuals within a group (Scott, 1991)
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Leading to cohesion, social pressure, cooperation,
solidarity
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Existence of ‘cliques’ “placing people in society”
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Role played by conflict and power
Combination of graph theory and sociological framework
Suggests SNA meets “a need for appropriate concepts to
use in understanding complex societies”
Graph Theory and SNA
Graph / network theory
A graph G(V,E) consists of a set of vertices V representing
individuals or objects and a set of edges E representing
relationships between the individuals or objects
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Directed and undirected graphs, valued graphs
Degree, indegree, outdegree
Density, connectedness, subgraphs
Average path length, cliquishness, clustering
Centrality, periphery
More basic concepts from Analytic Technologies