I’ve improved the presentation of my network model for global terrorist collaborations. You can take a look at the code on my github, and definitely follow the link to view the network in full.
CLICK HERE TO SEE THE FULL NETWORK.
Please note that I replaced the node IDs for “the” Taliban (T), Boko Haram (BH), ISIL/ISIS (IS), Hamas (H), and”the” Al-Qaeda (without a regional modifier in the name) (AQ) by the initials herein, so that they can be easily pinpointed on the graph. You’ll probably want to open the lengthy node key in another window.
A few notes:
Criteria for inclusion. Please refer to my previous post.
Node Clean-up. I got rid of the nodes “Unknown”, “Individual” (meaning a non-organization), and “Other”, which had escaped my attention and unduly linked some pairs of organizations as having one degree of separation (e.g. both Group A and Group B collaborated with persons who were never discovered– this doesn’t mean they collaborated with the SAME person!). I’m also noticing some nodes here and there that have basically the same problem, such as “Palestinians”– that is not an organization. I will return to these sorts of nodes and remove them on a case-by-case basis.
Community Detection. I used the “fast greedy” community detection algorithm to assign and color the communities. Here is a comparison of community detection algorithms for networks with various properties. Before executing this algorithm, I combined any multiple edges between a pair of nodes into a single weighted edge, and got rid of loops (since “collaboration with oneself” is not what I was intending to portray in this model).
Let’s take a look at the output given by R. Upon inspection, these groupings seem to make sense; the organizations seem plausibly affiliated and frequently refer to the same cultures, regions, or ideologies. Some of the names could use a bit of clarification (for example, “Dissident Republicans” refers to breakaways from the IRA toward the end of the Northern Ireland conflict) or expansion/compression. As you may infer, the numberings to the left of the members of a group are not the node IDs that appear in the rainbow graph later, but rather numberings within the communities (only the first number is shown per line of community members).
Cliques. The largest cliques (complete subgraphs) were revealed as:
Clique 1. Bangsamora Islamic Freedom Movement (BIFM), New People’s Army (NPA), Moro National Liberation Front (MNLF), Moro Islamic Liberation Front (MILF), Abu Sayyaf Group (ASG)
Clique 2. Popular Resistance Committees, Popular Front for the Liberation of Palestine (PFLP), Hamas, al-Aqsa Martyrs Brigade, Democratic Front for the Liberation of Palestine (DFLP)
Clique 3. Popular Resistance Committees (PFLP), Hamas, Al-Asqa Martyrs Bridgade, Palestinian Islamic Jihad (PIJ)
Centrality. I wanted to know how “influential” each node was. Of course, centrality is not the only way to measure this, especially in a case like the GTD where we have so much other information, such as victim counts. Even going on centrality, there are several centrality measure options in igraph for R; I went with eigencentrality. To quote from the manual:
“Eigenvector centrality scores correspond to the values of the first eigenvector of the graph adjacency matrix; these scores may, in turn, be interpreted as arising from a reciprocal process in which the centrality of each actor is proportional to the sum of the centralities of those actors to whom he or she is connected. In general, vertices with high eigenvector centralities are those which are connected to many other vertices which are, in turn, connected to many others (and so on).”
The “scale” option fixed a maximum score of 1.
Nodes Sorted by Eigencentrality (Decreasing) + Commentary: