Similarities between gold farming networks and drug dealing networks

Muhammad Aurangzeb Ahmad, a PhD student researching Computational Trust at the University of Minnesota, has posted research comparing the organizational structure of gold farmers in virtual worlds to drug dealers in the physical world. It's a fascinating analysis, showing the underlying similarities in networks of people who undertake prohibited activities.
Figures 5 and 6 compare the identified farmer network to the Caviar drug-trafficking network. Both the real world and virtual criminal networks exhibit very similar performance and resilience under degree attack and random failures. Removing fewer than 1% of the nodes by attack keeps the fraction of the network in the LCC (largest connected component) relatively high and the number of isolates in the network relatively low. However, these networks are an order of magnitude more sensitive to node removal than the affiliate networks analyzed in Figure 4; removing approximately 5% of nodes by degree attack cuts the fraction of nodes in the largest connected component below 50% while increasing the fraction of isolates to approximately 50%.

Taken together, this analysis shows the farmer and affiliate networks have substantial resilience to both random failures and determined attacks over several orders of magnitude before fracturing into many disconnected components, a pattern which is also found in a real-world drug trafficking network. The affiliate network composed of farmers, unidentified farmers, and legitimate players exhibits even less sensitivity to attack than the clandestine networks alone. These findings suggest that farmers are able to effectively conceal their interaction patterns against the background of legitimate trade activity which also provides substantial resilience to interdiction.

Gold Farming