In Bad Company: SNA as a tool for analyzing ‘dark’ networks

The term homophily pops up frequently in conversations regarding network analysis. Homophily, most simply, is a pithier way of saying “birds of a feather flock together”. While last week we investigated some of the more positive aspects that can be shared through networks, such as happiness or a sense of belonging, sometimes the company we keep is not as uplifting.

A great example of SNA’s applicability can be seen in the realm of hacking and other cybercrimes.  The proliferation of online networks exemplifies this shift from large, hierarchical crime structures to more disconnected pockets of actors. As such, the question that criminologists face is how to target high-profile actors that could disrupt the actions of these decentralized networks. In 2012, researchers from the University of Montreal set out to examine SNA’s ability to do just that. As the authors note, the sheer volume of manpower and resources involved in uncovering cybercrimes makes investigating every individual actor a wholly impractical endeavor. As such, these researchers wanted to use social network analysis not only to quantify the relationships between cybercriminals, but also see if the methodology could identify any persons of interests that investigators may have missed.

Using stored chat logs obtained from the hard drives of convicted hackers, one-on-one conversations were used to construct connections between 771 hackers. These connections were used to determine who among these actors would be considered persons of interest (in this case, defined as persons who were contacted by two or more of the convicted hackers from which the data was obtained).  The final network of hackers and POI constituted 38 actors out of the original sample. For these actors, degree and betweenness centrality measures were calculated to determine who among them was being consulted for their expertise, or who was providing avenues for communication between actors.

In the above figure, red nodes represent convicted hackers, whereas blue nodes represent POI (Décary-Hétu, D.& Dupont, B. 2012)

The above figure represents their final network. Their findings illustrate that this intimate network of hackers and POI are all likely to be in contact with one another, however this finding also implies that the 28 hackers who scored highly on these centrality measures could be just as influential as those that were convicted. This means that although the 10 arrests made were accurately targeted towards prominent actors, and their removal effectively disrupted communication within the network, targeting some of these POI may have done so even more effectively. As the authors put it, “the nail that sticks out gets hammered down.” What is notable, however, is that SNA measures were able to accurately identify those convicted as high profile actors with relative ease, as well as identify a myriad of other actors for further investigation.  The use of social network analysis could help lighten the monetary and cognitive load that more mainstream methods of online investigation may incur.

Just as social network analysis can help characterize the nature of these crime networks, it can also help elucidate the widespread nature of seemingly individualistic crimes. For example, to say that corruption is prominent within the United States Congress is not a contentious point, however how do we determine the magnitude of this corruption? Do congressman commit acts of corruption in solitude, or is there a more overarching culture of corruption in congress?

The 109th House was particularly damning, with three congressman being sentenced for taking bribes from PACS. Though taking money from PACs is obviously not illegal in and of itself, the Federal Bribery Statute clarifies that funding that influences lawmaker voting is seen as a criminal act. Following these indictments, several theoretical explanations arose; some argued that this was a case of a few bad apples who happened to be on the wrong side of the law; others argued that this was a partisan issue, considering all three congressman were Republicans; the majority of the American public, however, theorized that Congress was a wholly corrupt institution.

To test each of these theories, authors Clayton Peoples and James Sutton wanted to illustrate just how much PAC contributions had on voting behavior across all members of congress. In their own words, “is there a statistically significant general effect of shared PAC contributors on vote similarity among pairs of lawmakers in the 109th U.S. House, controlling for other factors?” By investigating relationships rather than individual behavior, their analysis would simultaneously address all three of the proposed “theories” about the prevalence and nature of corruption in congress. In order to quantify these relationships, the researchers turned to SNA.

The authors employed social network analysis in its strictest form. Dyadic ties between congressman were drawn based on their shared attributes in order to view relationships between congressman as units of analysis. Relationships such as party affiliation, shared committee membership, and most importantly shared PAC contributors, were calculated using matrices of dyadic ties. Then, using regression analysis, they controlled for most of these relationships, isolating shared PAC contributions as their independent variable.

Their findings were frightening, to say the least. They illustrated that, across the board, shared PAC contributions played a statistically significant role on the voting habits of lawmakers. An important note is that PAC contributions played a far more significant role than shared committee membership or memberships based on state; the authors use this to discern voting that could constitute a “bribe”, rather than votes that would have occurred with the interest of constituents in mind. This finding was consistent across party lines, decrying the notion that corruption was a purely Republican issue. Through the use of social network analysis, the researchers were able to illustrate that bribery, though typically defined as an individual act, could be a part of a larger network of collusion and institutional corruption that should be investigated as such. Given that these actions seemed to be normalized practice in the 109th congress, we can safely say that this was not the case of a few bad apples; as Décary-Hétu and Dupont put it, it was just the nail that stuck out that got hammered down.



Austin Round


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