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
‒ 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 socioeconomics
• 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
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?
Histogram – actor engagement
80
• 1750s – mid-1760s
– Decline in network membership; 7Years War with France; investment in
slave trade through drinking clubs
60
• 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
40
• 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
20
0
1750
1760
1770
1780
1790
1800
1810
Ascendancy in Atlantic
1756-1763
1765-1774
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!