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database. Hence, the graph model can be applied productively and effectively in many
network analysis use cases.
Consider this marketing a
ttribution use case: person A sees the marketing cam
paign;
person A talks about it on social media; person B is connected to person A and sees
the comment; and, subsequently, person B buys the product. From the marketing
campaign manager’s perspective, the standard relational model fails to identify the
attribution, since B did not see the campaign and A did not respond to the campaign.
The campaign looks like a failure, but its actual success (and positive ROI) is discov‐
ered by the graph analytics algorithm through the transitive relationship between the
marketing campaign and the final customer purchase, through an intermediary
(entity in the middle).
Next, consider an anti-money laundering (AML) analysis case: persons A and C are
suspected of illicit trafficking. Any interaction between the two (e.g., a financial trans‐
action in a financial database) would be flagged by the authorities, and heavily scruti‐
nized. However, if A and C never transact business together, but instead conduct
financial dealings through safe, respected, and unflagged financial authority B, what
could pick up on the transaction? The graph analytics algorithm! The graph engine
would discover the transitive relationship between A and C through intermediary B.
In internet searches, major search engines use a hyperlinked network (graph-based)
algorithm to find the central authoritative node across the entire internet for any
given set of search words. The directionality of the edge is vital in this case, since the
authoritative node in the network is the one that many other nodes point at.
With literature-based discovery (LBD)—a knowledge network (graph-based) applica‐
tion enabling significant discoveries across the knowledge base of thousands (or even
millions) of research journal articles—“hidden knowledge” is discovered only
through the connection between published research results that may have many
degrees of separation (transitive relationships) between them. LBD is being applied to
cancer research studies, where the massive semantic medical knowledge base of
symptoms, diagnoses, treatments, drug interactions, genetic markers, short-term
results, and long-term consequences could be “hiding” previously unknown cures or
beneficial treatments for the most impenetrable cases. The knowledge could already
be in the network, but we need to connect the dots to find it.
Similar descriptions of the power of graphing can be given for the other use cases lis‐
ted earlier, all examples of network analysis through graph algorithms. Each case
deeply involves entities (people, objects, events, actions, concepts, and places) and
their relationships (touch points, both causal and simple associations).
When considering the power of graphing, we should keep in mind that perhaps the
most powerful node in a graph model for real-world use cases might be “context.”
Context may include time, location, related events, nearby entities, and more. Incor‐
xiv | Foreword