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However, another plausible (though arguably less pleasant) outcome would be where
we take sides in the dispute between our “friends” creating two relationships with neg‐
ative sentiments—effectively ganging up on an individual. Now we can engage in gossip
about our mutual dislike of a former friend and the closure again becomes balanced.
Equally we see this reflected in the organizational scenario where Alice, by managing
Bob and Charlie, becomes, in effect, their workplace enemy as in
Figure 7-17
.
Mining Organizational Data in the Real World
We don't have to derive these graphs from analyzing organizational charts, because that's
a static and often inconsistent view of how an organization really works. A practical and
timely way of generating the graph would instead be to run these kinds of analyses over
the history of email exchanges between individuals within a company.
We'd easily be able to identify a graph at large scale and from that we'd be able to make
predictive analyses about the evolution of organizational structure by looking for op‐
portunities to create balanced closures. Such structures might be a boon—that we ob‐
serve employees are already self-organizing for a successful outcome; or they might be
indicative of some malpractice—that some employees are moving into shadowy corners
to commit corporate fraud! Either way, the predictive power of graphs enables us to
engage those issues proactively.
Balanced closures add another dimension to the predictive power of graphs. Simply by
looking for opportunities to create balanced closures across a graph, even at very large
scale, we can modify the graph structure for accurate predictive analyses. But we can
go further, and in the next section we'll bring in the notion of
local bridges
, which give
us valuable insight into the communications flow of our organization, and from that
knowledge comes the ability to adapt it to meet future challenges.
Local Bridges
An organization of only three people as we've been using is anomalous, and the graphs
we've studied in this section are best thought of as small subgraphs as part of a larger
organizational hierarchy. When we start to consider managing a larger organization we
expect a much more complex graph structure, but we can also apply other heuristics to
the structure to help make sense of the business. In fact, once we have introduced other
parts of the organization into the graph, we can reason about global properties of the
graph based on the locally acting strong triadic closure principle.
In
Figure 7-19
we're presented with a counterintuitive scenario where two groups in the
organization are managed by Alice and Davina, respectively. However, we have the
slightly awkward structure that Alice not only runs a team with Bob and Charlie, but
also manages Davina. Though this isn't beyond the realm of possibility (Alice may
