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Investigating networks over time: Matrixify

Investigating networks over time: Matrixify. John Haggerty University of Salford School of Computing, Science & Engineering Sheryllynne Haggerty University of Nottingham School of Humanities. Historians and networks. Historians have been analysing networks for some time

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Investigating networks over time: Matrixify

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  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 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

  8. Network ‘Shape’ Actor Time • 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?

  9. Histogram – actor engagement 80 • 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 60 40 20 0 1750 1760 1770 1780 1790 1800 1810

  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!

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