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Investigating networks over time: Matrixify
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Investigating networks over time: Matrixify
John HaggertyUniversity of Salford
School of Computing, Science & Engineering
Sheryllynne HaggertyUniversity of Nottingham
School of Humanities
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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
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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)
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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)
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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.
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Matrixify overview
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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
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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
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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
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Ascendancy in Atlantic1756-17631765-1774
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Effect of 1772 credit crisis
1770-1772;1773-1775
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Effect of American War
1776-1780;1781-1785
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Effect of 1793 credit crisis
1791-1793;1794-1796
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Abolition of slave trade
1804-1806;1807-1809
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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
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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
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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!