investigating networks over time: matrixify john haggerty university of salford school of computing,...
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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
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
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)
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)
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.
Matrixify overview
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
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
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
Ascendancy in Atlantic1756-17631765-1774
Effect of 1772 credit crisis
1770-1772;1773-1775
Effect of American War
1776-1780;1781-1785
Effect of 1793 credit crisis
1791-1793;1794-1796
Abolition of slave trade
1804-1806;1807-1809
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
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
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!