  A text network refers to a data type that each vertex is associated with a
text document and the relationship between documents is represented by edges.
The proliferation of text networks such as hyperlinked webpages and academic
citation networks has led to an increasing demand for quickly developing a
general sense of a new text network, namely text network exploration. In this
paper, we address the problem of text network exploration through constructing
a heterogeneous web of topics, which allows people to investigate a text
network associating word level with document level. To achieve this, a
probabilistic generative model for text and links is proposed, where three
different relationships in the heterogeneous topic web are quantified. We also
develop a prototype demo system named TopicAtlas to exhibit such heterogeneous
topic web, and demonstrate how this system can facilitate the task of text
network exploration. Extensive qualitative analyses are included to verify the
effectiveness of this heterogeneous topic web. Besides, we validate our model
on real-life text networks, showing that it preserves good performance on
objective evaluation metrics.
