Database Reference
In-Depth Information
Telephone Networks
Here the nodes represent phone numbers, which are really individuals. There is an edge
between two nodes if a call has been placed between those phones in some fixed period of
time, such as last month, or “ever.” The edges could be weighted by the number of calls
made between these phones during the period. Communities in a telephone network will
form from groups of people that communicate frequently: groups of friends, members of a
club, or people working at the same company, for example.
Email Networks
The nodes represent email addresses, which are again individuals. An edge represents the
fact that there was at least one email in at least one direction between the two addresses.
Alternatively, we may only place an edge if there were emails in both directions. In that
way, we avoid viewing spammers as “friends” with all their victims. Another approach is
to label edges as
weak
or
strong
. Strong edges represent communication in both directions,
while weak edges indicate that the communication was in one direction only. The com-
munities seen in email networks come from the same sorts of groupings we mentioned in
connection with telephone networks. A similar sort of network involves people who text
other people through their cell phones.
Collaboration Networks
Nodes represent individuals who have published research papers. There is an edge between
two individuals who published one or more papers jointly. Optionally, we can label edges
by the number of joint publications. The communities in this network are authors working
on a particular topic.
An alternative view of the same data is as a graph in which the nodes are papers. Two
papers are connected by an edge if they have at least one author in common. Now, we form
communities that are collections of papers on the same topic.
There are several other kinds of data that form two networks in a similar way. For ex-
ample, we can look at the people who edit Wikipedia articles and the articles that they edit.
Two editors are connected if they have edited an article in common. The communities are
groups of editors that are interested in the same subject. Dually, we can build a network
of articles, and connect articles if they have been edited by the same person. Here, we get
communities of articles on similar or related subjects.
In fact, the data involved in collaborative filtering, as was discussed in
Chapter 9
, often
can be viewed as forming a pair of networks, one for the customers and one for the
products. Customers who buy the same sorts of products, e.g., sciencefiction topics, will
