18 research outputs found
Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology
Topological data analysis is an emerging area in exploratory data analysis
and data mining. Its main tool, persistent homology, has become a popular
technique to study the structure of complex, high-dimensional data. In this
paper, we propose a novel method using persistent homology to quantify
structural changes in time-varying graphs. Specifically, we transform each
instance of the time-varying graph into metric spaces, extract topological
features using persistent homology, and compare those features over time. We
provide a visualization that assists in time-varying graph exploration and
helps to identify patterns of behavior within the data. To validate our
approach, we conduct several case studies on real world data sets and show how
our method can find cyclic patterns, deviations from those patterns, and
one-time events in time-varying graphs. We also examine whether
persistence-based similarity measure as a graph metric satisfies a set of
well-established, desirable properties for graph metrics
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
A visual multivariate dynamic egocentric network exploration tool
Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this thesis, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, distances between projected ego-networks represent the dissimilarity across temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year long credit card transaction