The Montreal Street Gang Network, originally constructed and
analysed by Katine
Descormiers and Carlos Morselli (2011) has also been the focus of
further studies. These have demonstrated that when looking at the
relationships between different gangs in the city, shared
characteristics or attributes do not have a significant relationship in
affecting the probability of tie formation (Wallin,
2021). This is interesting, as other studies on gang networks have
pointed to a lack of structural analysis into the role of clustering, or
triad closures, which leads to an overestimation)) of the role of
homophily (Grund
& Densely, 2015). However, as this network homophily does not
seem to play a statistically significant role, the question remains on
the role of clustering, meaning if the gangs are connected to each other
and each is also connected to a third gang forming a triad closure.
Therefore, this investigation will deal with the following
hypothesis:
-H1: Gangs with similar attributes, in this case ethnicity,
territories, and allegiances, are more likely to form a relationship
with each other. (based on Wallin,
2021).
-H2: Gangs’ relationships are formed through clustering.
Data Overview and Visualisation
The data found in this network was gathered from interviews with
gang members in Montreal in 2007, which at the time had identified 35
active gangs in the city (Descormiers
& Morselli, 2011). The data also included the attributes on
where they operate within Montreal (Downtown, East, West), the
ethnicities of the gangs (Afro-Canadian, Asian, Caucasian, Hispanic,
Mixed), their allegiance to bigger groups of gangs present in the city
(Bloods, Crips, or Other). This network is not directed or weighted,
meaning that ties represent the relationships between gangs as reported
by the gang members during the focus groups and interviews, and
therefore do not indicate if the relationships are positive or negative,
or any type of hierarchical or influence/power dynamics present in this
context. In order to best visualise the network for the purpose of this
investigation, the choice was to use a 3D visualisation of the network
(below) in which one is to select specific nodes and see their
edges.
Modelling
In order to address the hypothesis posed above, this investigation
will use an exponential family random graph model (ERGM). Firstly, to
understand whether gangs’ attributes (e.gallegiance, ethnicity and
territory) have an effect in the formation and number of their
relationships with other gangs, we employed nodal covariates using the
‘nodefactor()’ term (Social
Network Analysis for Anthropologists, n.d.). Additionally, in order
to address our second hypothesis on clustering as a structural factor,
we have employed a ‘gwesp()’ (with an alpha of 0.5) as it represents a
more recent approach to modelling triad closure.
Results
ERGM results table
Model1
edges
-3.93***
(0.34)
nodematch.Ethnicity
-0.35
(0.29)
nodematch.Territories
0.20
(0.22)
nodematch.Allegiances
-0.16
(0.31)
gwesp.fixed.0.5
1.34***
(0.20)
AIC
403.52
BIC
425.46
Log Likelihood
-196.76
***p < 0.01; **p < 0.05; *p <
0.1
In terms of H1, we see that we cannot reject the null hypothesis, as
there in no attribute has a significant effect on the formation of
relationships between the gangs in the network, which is not surprising
as it had already between demonstrated by previous investigations (Wallin,
2021). Differently, however, in terms of H2, we can reject the null
hypothesis as triad closures have a significant effect on relationship
formation between gangs within this network.
Markov Chain Monte Carlo (MCMC)
In general, the model converged well with 99% confidence in the 13th
iteration of 60. This is further observed in the sample statistics
above, which show mostly normal distribution. However, in some of them
we see negative skewness (left-skewed), thus, making them asymmetrical
distributions. This, though perhaps uncomfortable to the eye is not
necessarily an issue, however, one could run a distribution test to
identify the distribution of your data.
Diagnostics: Goodness-of-fit (GOF)
As observed in the plots above, the observed values are generally
within the range of the simulated values, with some instances in which
the values are overestimated or underestimated. On this, some fixes were
attempted, however, to little effect. It is also important to keep in
mind that there was some missing data in terms of the three attributes
which might affect the goddess of fit of the present data and
network.
Conclusion
In conclusion, this investigation has, whilst connecting with the
original authors and other studies, managed to confirm previous on
homophily not being a factor for tie formation between the gangs within
this network. We have also managed to connect with social networks
literature, and demonstrate in fact, a structural factor, in this case
clustering was better at explaining the formation of ties. This is
interesting, and it further confirms general ideas about networks. It
would be interesting to see how this network has evolved over time, and
if these closures have remained the same or not. Furthermore, making
this a directed network could bring additional insights.