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

2. 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

3. 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)

4. 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)

5. 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.

6. Matrixify overview

7. Case study Liverpool was 2 nd 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

8. 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

9. 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 1750176017701780179018001810 0 40 80 20 60

10. Ascendancy in Atlantic 1756-1763 1765-1774

11. Effect of 1772 credit crisis 1770-1772; 1773-1775

12. Effect of American War 1776-1780; 1781-1785

13. Effect of 1793 credit crisis 1791-1793; 1794-1796

14. Abolition of slave trade 1804-1806; 1807-1809

15. 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

16. 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

17. 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!