iTopic: Influential Topic Discovery from Information Networks via Keyword Query

The rapid growth of information networks provides a significant
opportunity for people to learn the world and find useful information
for decision making. To find influential topics in a given
context, instead of searching widely over the whole information
network, normally it is wise to find the related communities first
and then identify the influential topics in those communities. In
this demonstration, we present a novel framework to compute the
correlated sub-networks from a large information network such as
CiteSeerX based on a user’s keyword query, and to extract the influential
topics from each correlated network. To help users understand
the influential topics as a whole, we utilize a word cloud
to represent the discovered topics for each correlated network. As
such, multiple word clouds can be generated for different correlated
networks, by which users can easily pick up their interested
ones by reading the visualized topic descriptions over word clouds.
To determine the sizes of different terms in a word cloud, we introduce
a scoring scheme for assessing the influence of these terms
in the corresponding networks. We demonstrate the functionality
of our influential topic system, called iTopic, using the CiteSeerX
information network data.