DyEgoVis: Visual Exploration of Dynamic Ego-Network Evolution

Kun Fu,Yang Wang,Xian Sun,Yuanben Zhang,Tingyun Mao,Daoyu Lin

doi:10.3390/app11052399

Abstract

Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and is influenced by the outside world. However, most of the existing methods do not fully consider the multilevel analysis of dynamic ego-networks, resulting in some evolution information at different granularities being ignored. In this paper, we present an interactive visualization system called DyEgoVis which allows users to explore the evolutions of dynamic ego-networks at global, local and individual levels. At the global level, DyEgoVis reduces dynamic ego-networks and their snapshots to 2D points to reveal global patterns such as clusters and outliers. At the local level, DyEgoVis projects all snapshots of the selected dynamic ego-networks onto a 2D space to identify similar or abnormal states. At the individual level, DyEgoVis utilizes a novel layout method to visualize the selected dynamic ego-network so that users can track, compare and analyze changes in the relationships between the ego and alters. Through two case studies on real datasets, we demonstrate the usability and effectiveness of DyEgoVis.

Highlights

Networks (a.k.a. graphs) are ubiquitous data that can be used to model relationships between entities such as communications between e-mails, partnerships between companies, friendships between Facebook users, and interactions between proteins
We propose DyEgoVis, an interactive visualization system that allows users to explore the evolutions of dynamic ego-networks at the three analysis levels
We focus on the visual exploration of dynamic ego-networks in a dynamic network

Summary

Introduction

Networks (a.k.a. graphs) are ubiquitous data that can be used to model relationships between entities such as communications between e-mails, partnerships between companies, friendships between Facebook users, and interactions between proteins. Based on different analysis perspectives, dynamic network analysis can be divided into whole-network analysis and egocentric analysis [1]. The former displays the structure of the entire dynamic network to reveal global evolution patterns such as cluster formation and separation. Different from the former, the latter focuses on a local sub-network centered on a focus node and analyzes its evolutionary process. This sub-network is called an ego-network which consists of a focus node (called ego), its neighbors (called alters), and all the connections among these nodes. A snapshot is an evolution state of the dynamic ego-network at the timestep

Objectives

Discussion

Conclusion