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Investigati ng networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University of Nottingham School of Humanities

Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

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Page 1: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Investigating networks over time: Matrixify

John HaggertyUniversity of Salford

School of Computing, Science & Engineering

Sheryllynne HaggertyUniversity of Nottingham

School of Humanities

Page 2: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Historians and networks

• Historians have been analysing networks for some time‒ Often thought networks are positive due to

focus on ethnic, familial or religious ties

• More complex story? e.g.‒ Actor (in)activity in the network‒ Why are actors involved at particular times?‒ Dynamic network membership (power, density,

cliques)‒ Endogenous and exogenous

Page 3: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Social network characteristics

• Historians have borrowed from socio-economics

• Social network relational power– ‘Weak’ vs. ‘strong’ ties (Granovetter 1973)

• Relationships can be assessed/measured– Centrality (Freeman, 1978/79)

• People ‘invest’ in networks– Social capital (Bourdieu, 1985; Portes, 1998)

Page 4: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Static vs Temporal SNA

• What can Computer Science add to analysis?• Static SNA

– Aggregated data– Snapshot of network during time period– Micro view of network (part of the network at a

specified time)

• Temporal SNA– Non-aggregated data– Analysis of change over time– Macro view of network (actor engagement and

overall network trends)

Page 5: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Matrixify SNA software

• Static SNA tools alone (e.g. Pajek) do not fully meet historians’ needs– ‘Change over time’ question

• Matrixify (Haggerty & Haggerty, 2011)1

– Visualisation of temporal network events– Simple interface with sophisticated analysis– No scripting– Exploratory analysis (raise questions)– In-built static SNA to explore network events

1. Haggerty & Haggerty (2011), “Temporal Social Network Analysis for Historians: A Case Study”, Proceedings of IVAPP 2011, pp. 207-217.

Page 6: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Matrixify overview

Page 7: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Case study

• Liverpool was 2nd port city– Experienced growth in domestic and

international trade

• Company of African Merchants Trading from Liverpool (‘African Committee’) – Predominantly slave traders– Includes leading Liverpool businessmen and

council members during the period– Approx. 280 individual members during this

period

Page 8: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Network ‘Shape’

• Actor involvement– Why some for short time, others not? Do they network elsewhere? Do long-term

actors dominate the network?

• Network density– Why is the network more dense in particular periods (1770s, 1780s, early

1790s)? Why significant change in 1790s?

• Endogenous and exogenous events– Why lesser involvement in 1750s, 1760s and 1800s? Actors using other

formal/informal networks?

Time

Actor

Page 9: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Histogram – actor engagement

• 1750s – mid-1760s– Decline in network membership; 7-

Years War with France; investment in slave trade through drinking clubs

• Mid-1760s – mid-1790s– Rise in network membership; Britain in

ascendancy in Atlantic; War of Independence in America; rise in investment in slave trade through AC

• Mid-1790s – 1810– Sudden decline in network

membership; start of Napoleonic Wars; 1793 credit crisis; Abolition of Slave Trade 1807; investment in slave trade outside AC and among smaller investment networks 1750 1760 1770 1780 1790 1800 1810

0

40

80

20

60

Page 10: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Ascendancy in Atlantic1756-17631765-1774

Page 11: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Effect of 1772 credit crisis

1770-1772;1773-1775

Page 12: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Effect of American War

1776-1780;1781-1785

Page 13: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Effect of 1793 credit crisis

1791-1793;1794-1796

Page 14: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Abolition of slave trade

1804-1806;1807-1809

Page 15: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Temporal SNA findings

• Actor (in)activity?– Actors engaged with the network when it was

beneficial to do so

• Engagement affected by exogenous events– Wars, credit crises and national events had

differing effects– Engagement reflects confidence in trade– Certain events have greater or lesser effect

on the network

Page 16: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Temporal SNA findings

• Endogenous events affecting the network?– No qualitative information for this data set

collected as yet

• Life cycle of networks– Various networks in play at any one time

• As some whither, others rise in ascendancy

– Reflects changes in the wider business environment

– Affects ability of the network to react to exogenous effects

Page 17: Investigating networks over time: Matrixify John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University

Conclusions

• Social networks are complex

• Historians require tools that answer a key issue – ‘change over time’

• Temporal SNA provides macro-view of network dynamics

• Matrixify integration of tools allows ‘drilling down’ to explore key issues– …IMPORTANTLY will raise questions rather

than answer them!