Information Technology Reference
In-Depth Information
groups to improve knowledge creation and sharing [5]. Mapping and understand-
ing social networks within an organization is a mean for us to understand how so-
cial relationships may affect business processes. To understand the complexity of
the task, let us consider the various structural measures that can be applied to so-
cial networks. These structures are characterized by relationships, entities, context,
configurations, and temporal stability. Some of the indices and dimensions that
express outcomes of network are:
1.
Size: density and degree. Size is critical for the structure of social relations
due to each actor has limit resources and for building and maintaining ties.
The degree of an actor is defined as the sum of the connections between the
actor and others. The density measurement can be used to analyze the con-
nectivity and the degree of nodes and links in a social network [14].
2.
Centrality: The centrality of a social network is a measurement that is used
to measure the betweenness and closeness of the social network. The meas-
ure of centrality which can be used to identify who have the most connec-
tions to others in the network (high degree) or whose departure would cause
the network to fall apart [14].
3.
Structural hole: The structural hole is also a measurement of social network
analysis, which can be used to discover the holes in a social network and by
this to fill the hole and expand the social network [14].
4.
Reachability: The reachability can be used to analyze how to reach a node
from another node in the social networks. An actor is reachable by another if
there exists any set of connections by which we can trace from the source to
the target actor, regardless of how many others fall between them [7].
5.
Distance. Because most individuals are not usually connected directly to
most other individuals in a population, it can be quite important to go be-
yond simply examining the immediate connections of actors, and the overall
density of direct connections in populations. Walk, trail and path are basic
concepts to develop more powerful ways of describing various aspects of
the distances among actors in a network[4] [12].
2.2 Data Mining
Data mining has given the cleaned data intelligent methods that can be applied in
order to extract data patterns. Data Mining is the extraction of hidden predictive
information from large databases, is a powerful new technology with great poten-
tial to help companies focus on the most important information in their huge data-
base [15].
Data mining technologies can be use to generate new business opportunities by
providing capabilities if given databases of sufficient size and quality: automatic
prediction of trends and behaviors, and automatic discovery of previously un-
known patterns. The mostly common used techniques in data mining are listed as
followings [15]:
