NetReAct: Interactive Learning for Network Summarization

TL;DR

NetReAct is an interactive algorithm that uses human feedback and reinforcement learning to improve network summaries and visualizations, aiding sensemaking in document similarity networks.

Contribution

It introduces a novel interactive network summarization method that incorporates human feedback through reinforcement learning for improved visualization quality.

Findings

NetReAct outperforms baseline methods in generating high-quality summaries.

It effectively reveals hidden patterns in document networks.

Human feedback enhances visualization accuracy.

Abstract

Generating useful network summaries is a challenging and important problem with several applications like sensemaking, visualization, and compression. However, most of the current work in this space do not take human feedback into account while generating summaries. Consider an intelligence analysis scenario, where the analyst is exploring a similarity network between documents. The analyst can express her agreement/disagreement with the visualization of the network summary via iterative feedback, e.g. closing or moving documents ("nodes") together. How can we use this feedback to improve the network summary quality? In this paper, we present NetReAct, a novel interactive network summarization algorithm which supports the visualization of networks induced by text corpora to perform sensemaking. NetReAct incorporates human feedback with reinforcement learning to summarize and visualize document networks. Using scenarios from two datasets, we show how NetReAct is successful in generating high-quality summaries and visualizations that reveal hidden patterns better than other non-trivial baselines.