Databases Reference
In-Depth Information
Today, Talent.net depends on users having supplied information regarding their inter‐
ests, skills, and work history before it can infer their professional social relations. But
with the core inferential capabilities in place, the platform is set to generate even greater
insight for less end-user effort. Skills and interests, for example, can be inferred from
the processes and products surrounding people's day-to-day work activities. Writing
code, writing documents, exchanging emails: activities such as these require interacting
with systems that allow us to capture hints as to a person's skills. Other sources of data
that help contextualize a user include group memberships and meetup lists. Although
the use case presented here does not cover these higher-order inferential features, their
implementation requires mostly application integration and partnership agreements
rather than any significant change to the graph or the algorithms used.
Talent.net data model
To help describe the Talent.net data model, we've created a small sample graph, as shown
in
Figure 5-1
, which we'll use throughout this section to illustrate the Cypher queries
behind the main Talent.net use cases.
Figure 5-1. Sample of the Talent.net social network
