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Social Network Analysis

Mark Round

+44 (0) 1684 89 4450

MDRound@QinetiQ.com

Date: 04 Dec 2007Location: BCS North London Branch

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SNA Overview 2

‘SocialNetworks’

xkcd.com

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SNA Overview 3

reproduced with permission from John Robb, GlobalGuerillas.com

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SNA Overview 4

Origins of SNA

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SNA Overview 5

Simplified SNA process

Collate Prepare Analyse Visualise Plan

& Collect

Interpret Advise

DYNAMICS:infer missing datawhy links formgrowth processesvulnerabilitiesrobustness

METRICS:central linkscentral actors roles & positionssubgroupscentralisation

INTEL & ADVICE:group structurelines of influencestabilisation pointsintervention pointsoptions

NETWORK MODEL

INFORMATION:Data mining resultsEntity extractionBayesian analysis

RAW DATA:interview datasurvey resultsobservationssystem logs

SOCIOGRAMS

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SNA Overview 6

• What are you trying to explain?

– variation in success, e.g. performance?

– homogeneity, in attitudes, beliefs or practices?

• Why does tie structure matter? Is it because they are:

– pipes, or

– girders

• What is the currency? Paperbacks, parcels, viruses, ideas, gossip?

– How does each diffuse: is it ‘copied’ or ‘moved’?

– What logic guides the routing of them?

Borgatti, S.P. & Foster, P.C. The Network Paradigm in Organizational Research: …. Journal of Management, 29(6) (2003) 991–1013

Borgatti, S.P., Centrality and Network Flow' Social Networks, 27, (2005) 55-71

Plan: What to ask before conducting an analysis

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SNA Overview 7

Collect

Collect/collate: relationships traced in system logs

ORGANISATIONAL DATA

called,

emailed,

sought help from,

invited,

met with,

shared a space with,

…

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SNA Overview 8

Prepare/Visualise: varieties of actors & relationships

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SNA Overview 9

Data: QinetiQ info flow questionnaire

Caveat: Borgatti (2005) On the robustness of centrality measures under conditions of imperfect data, Social Networks, 28, 2

Analyse: betweenness (centrality) – I…

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SNA Overview 10

A B

DATA:

QinetiQ staff,Problem-Solving questionnaire

Analyse/Interpret: discovering brokers (boundary spanners)

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SNA Overview 11

Analyse: betweenness (centrality) – 7/7 attacks

Jordan, F. personal communication

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SNA Overview 12

m-slicing results:

Case Study:JFCOM MNE4 Week 1[Planning]

IWS m-slice 96:single network component

Components

Control

KnowledgeKnowledgeSupportSupport

Planning

Assessment

Execution

InteragencyInteragencyGroupGroup

Command

Interpret: structures & underlying processes

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SNA Overview 13

m-slicing results:

Case Study:JFCOM MNE4 Week 3 [Execution]

IWS m-slice 84:single network component

Components

Control

KnowledgeKnowledgeSupportSupport

Planning

Assessment

Execution

Command

Comp’ts integrated; Inter-agency distrib;WG appears; EBP still centralExecut’n on a limbMNIG gone native.

(but, caution…)

WG © Copyright QinetiQ

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SNA Overview 14

Collaboration tool logs, MNE4 Wk3 (link_wt>50)

IWS (blue), vs. random network (pink): ‘bimodal’ - suggests robustness to both

targeted attack & random failure

Degree Distribution (GT50, N=169)

0

10

20

30

40

50

60

70

80

6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96

Degree (upper bound)F

req

uen

cy

Interpret: Assessing robustness: JFCOM experiment: results

Robustness to targeted attack & random failure - rare

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SNA Overview 15

Removal of top 20% (degree) - small increase in diameter

Interpret/Advise: robustness - simulated attack

Collaboration tool logs (IWS/Wk3-GT50)

Simulated attack fails to disconnect network

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SNA Overview 16

• Difficulties in inferring causality – e.g. groups affect individuals, and vc. vs.

• Dynamic (predictive) modelling: statistical models, simulations

• Multi-modal networks (e.g. documents-people-groups-organisations)

• Missing data / overlapping networks (email vs. chat vs. observations)

• ‘Valued’ links – extracting value from weighted data

• Fragility & over-use of some measures

• Impact of spatial dimension

• Danger of cognitive bias in analysts

• Design and synthesis of networks

• (absence of a) Library of networks (network corpus), for ‘diagnosis’

• Analysis of very large networks

• Analysis tools are academic: powerful, borderline-usable, data formats…

Challenges & opportunities in SNA, in these domains

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SNA Overview 17Network analysis tools (yellow) and formats (blue) !

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