Database Reference
In-Depth Information
average predictability in user mobility, an exceptionally high value rooted in the
inherent regularity of human behavior. The most surprising is the lack of vari-
ability in predictability across the population, obtained by explored impact of
home, language groups, population density, and rural versus urban environment.
Although the population has an inherent heterogeneity, the maximal predictabil-
ity
max
varies very little; there are no users whose predictability would be
under 80%.
Knowing the history of a person's movements, the advanced pattern mining
techniques described in Chapters
6
and
7
can be used to find patterns and
regularities in human mobility, and to foresee his or her current location with
extremely high success probability.
15.2 Social Networks and Human Mobility
In the previous section we presented the evolution of the study on human mobil-
ity, describing the main patterns andmodels that characterize the mobility behav-
ior of individuals. Here, we take a step further in our journey of understanding
human behavior by focusing on the interplay between human mobility and social
networks, with the purpose of highlighting to what extent human movements
affect social dynamics, and how social interactions influence the way people
move.
We will first present a brief overview of network science and its growth in
the last decade, and then we will focus on recent developments and discoveries
regarding the interplay between the social world and the mobility of people.
15.2.1 Introduction to Network Science
Network science is a truly interdisciplinary field that examines the interconnec-
tions among diverse physical, engineered, information, biological, cognitive,
semantic, and social systems. In mathematical terms, a network is represented
by a graph
G
={
V,E
}
, where
V
is a set of
n
nodes and
E
is a set of edges that
connect
V
. According to the definition, any system of interacting elements can
be represented as a network. The mode of thinking of complex networks was
traditionally dominated by random graph theory, first proposed by Erdos and
Renyi in the 1950s. The random graph model presented a simple realization of
a network: we start with
N
disconnected nodes, and randomly connect every
pair of nodes with probability
p
, yielding a graph with
pN
(
N
−
1)
/
2 edges.
As data regarding wiring diagrams of real systems started being collected by
computer programs in late 1990s, topological information about real networks
became increasingly available, prompting many scientists to ask a fundamental
question: are real networks, from cell to Internet, truly random? Over the past
decade, we have witnessed dramatic advances along this direction, leading to
