A Visual Analytics Framework for Contrastive Network Analysis

TL;DR

This paper introduces ContraNA, a visual analytics framework that combines machine learning and visualization to compare complex networks and identify unique features, demonstrated through real-world case studies and a user study.

Contribution

The paper presents ContraNA, a novel framework integrating network representation and contrastive learning with visualization for network comparison tasks.

Findings

Participants effectively identified unique network features.

ContraNA improved interpretability of network differences.

The framework was validated with real-world datasets and user study.

Abstract

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL.