Text Network Exploration via Heterogeneous Web of Topics

TL;DR

This paper introduces a probabilistic model and a demo system for exploring text networks by constructing a heterogeneous web of topics, linking words and documents to facilitate understanding of complex networks.

Contribution

It presents a novel probabilistic generative model for creating a heterogeneous web of topics that links words and documents in text networks, along with a prototype system for exploration.

Findings

The model effectively captures relationships in text networks.

The demo system aids in intuitive exploration of complex text data.

Model performs well on real-world text network datasets.

Abstract

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.